AI-driven personalization of food systems: From precision nutrition to climate-resilient supply chains

Abstract

Traditional large-scale production and standardized interventions cannot address the risks due to individual metabolic diversity, climate change, and supply chain disruptions. This article reviews how artificial intelligence (AI) technology synergistically enhances precision nutrition interventions and climate-resilient supply chain resilience through cross-scale data integration and algorithm optimization, breaking away from the fragmented analytical approach that traditionally separates nutrition science from supply chain management. Simultaneously, the article promotes the establishment of cross-sectoral governance mechanisms to foster a virtuous cycle between AI technological advancement and environmental sustainability and food security, thereby positively impacting the sustainable and secure development of food systems. The Web of Science, Scopus, and PubMed were used as primary databases, focusing the search on AI applications within precision nutrition or climate-resilient supply chains. Priority was given to studies simultaneously addressing “data integration”, “algorithm optimization”, and “application effectiveness evaluation”. In precision nutrition, AI uses multi-omics and real-time data to develop personalized diets (e.g., predicting blood glucose). For supply chains, AI-driven climate modeling, dynamic logistics, and circular technology should reduce waste and increase resilience. Cases such as Singapore’s vertical farming and IBM’s Food Trust show AI’s potential, but data silos, algorithmic biases, and technology gaps hinder adoption. In the future, interpretative AI tools will need to be developed, and a cross-disciplinary, data-sharing platform established. Using policy synergy approaches, incentives should be developed and promoted to optimize the relationship between nutrition and climate, thereby moving the food system towards hyper-personalization and adaptability.

Introduction

The global food system is currently facing many crises, among them are nutritional imbalance and vulnerable supply chains, as shown below. The issue of nutritional imbalance shows a polarized pattern and has a wide-ranging impact. More than 3 billion people struggle to access healthy diets due to economic factors, resulting in micronutrient deficiencies and stunted growth¹. On the other hand, excessive consumption of ultra-processed foods in high-income countries has exacerbated the obesity epidemic². Yet “one-size-fits-all” dietary guidelines disregard individual metabolic differences. The same diet can result in blood glucose responses that vary by up to tenfold³.

The vulnerability of the food supply chain continues to increase, with significant impacts from external factors. Climate change and geopolitical conflicts, such as the war in Ukraine, have led to sharp fluctuations in the prices of important crops, threatening food accessibility⁴. The COVID pandemic has further exposed a basic contradiction within supply chains: the growing diversity of precise nutritional needs versus the supply chain’s heavy reliance on monoculture production. This contradiction has directly led to the persistent deterioration of nutritional status in low-income communities⁵. Moreover, traditional large-scale, intensive agricultural production models—such as continuous wheat cropping in India’s Punjab state—not only reduce the climate resilience of agricultural systems but also trigger issues like resource depletion, further undermining supply chain stability^6,7.

The basic model of the traditional food system has fundamental flaws. Mass production and standardized nutrition models overlook both the impact of dynamic climate changes on agricultural production and the variability in individual metabolism, making these models ill-equipped to address the complex scenarios arising from the convergence of multiple crises. The transformation of the food system has become an inevitable trend, requiring comprehensive solutions that span the entire process from production to consumption⁸, as shown in Fig. 1.

Fig. 1. Conceptual framework for including new food system technologies.

The figure provides an overview of various food system technologies that drive the transformation of food systems at different points along the food supply chain⁸.

AI technology provides a new perspective on the collaborative optimization of nutrition, health, and supply chain resilience. Its ability to integrate heterogeneous data from multiple sources enables multiple optimizations. In nutrition, for example, personalized interventions have been shown to lower postprandial blood glucose levels by up to 21%⁹. With supply chains, dynamic path planning has been found to reduce transportation carbon emissions by 15–30%¹⁰. For example, the IBM Food Trust platform has reduced supply chain waste by 30%¹¹, and technological innovations such as vertical farming have also reduced waste. However, there are important limitations to the current applications. In nutrition and supply chains, for example, barriers relating to data format and obtaining permission hinder full-chain modeling. Regions such as sub-Saharan Africa face issues of scalability and equity, and “data colonialism” increases the technological divide¹². The “black box” nature of AI can lead to algorithmic bias, threatening nutritional equity¹³.

Traditional large-scale, standardized governance models struggle to adapt to individual metabolic diversity and external dynamic changes such as climate and geography. Given the aforementioned challenges, limitations in AI applications, and basic demands highlighted by existing research—namely that “AI should be integrated throughout the entire supply chain to support long-term forecasting and food safety” and that “AI and digitalization must be extended across the entire value chain from farmers to consumers to achieve sustainable food systems” have led to AI not yet coming close to its potential^14,15. Existing research has not effectively aligned with this orientation. The integration of AI into food systems represents an emerging frontier, yet existing literature remains fragmented between precision nutrition and supply chain resilience, often overlooking their critical interconnections and the systemic trade-offs that arise from integration. Prior reviews have comprehensively explored AI applications in isolated domains, such as enhancing food safety and traceability¹⁴, promoting sustainable food system digitization¹⁵, or transforming agricultural productivity and supply chain operations¹⁶. However, a cohesive framework that synergizes hyper-personalized nutrition with climate-adaptive supply chains, while carefully examining the ethical, environmental, and equity implications of such integration, is notably absent. This gap is important, as optimizing one dimension in isolation may inadvertently exacerbate challenges in another—for example, personalized diets may increase carbon footprints, or AI scaling may deepen digital divides. The separate approaches fail to address the basic contradiction between nutritional demand and supply, and hinder the full realization of AI’s integrated advantages across the entire value chain.

Thus, this study proposes an “AI-driven, dual-loop, collaborative framework”, an integrated system solution with AI as the basic engine that dynamically couples and mutually empowers two objectives: “individual nutritional health optimization” and “enhancement of climate resilience in food supply chains.” Through multi-source data integration, cross-scale algorithm optimization, and end-to-end collaborative governance, this framework addresses the fundamental contradiction of traditional food systems that neglect individual metabolic diversity, climatic dynamics, and cross-linkage coordination. Therefore, this article fills the gap in the literature where AI applications for precision nutrition and supply chain management have been studied separately.

Human-computer interactions are part of a complex process in which various factors determine whether individuals accept the use of AI devices in their daily lives¹⁷. Thus, the target demographic for AI-driven precision nutrition highlighted in this review paper consists of individuals with a certain level of economic security who can readily access smart services and diverse food ingredients (such as the upper-middle class in Western countries and the urban middle class in emerging markets). It is important to clarify that for populations facing economic barriers to accessing healthy diets—including nutritionally vulnerable groups such as subsistence farmers—the basic priority remains ensuring adequate basic nutrition. AI-optimized personalized dietary plans are not currently the optimal approach for these demographics.

A multi-scale, dynamic, coupled model integrating metabolomics, climate, and logistics data streams has been constructed from “cell to global” to enable closed-loop optimization. Explainable AI (XAI) tools have been developed to improve the transparency of nutritional interventions¹⁸ and increase the credibility of decision-making processes. A policy mechanism based on an AI-driven “nutrition–climate” linkage should be designed to incorporate regional dietary subsidy algorithms and dynamic pricing of carbon capture. A technology transfer agreement should be established between the developed and developing countries to ensure that low-income countries have fair access to AI tools. This framework could provide innovative solutions for the sustainable development of the global food system.

AI in precision nutrition: from molecules to diets

Multi-omics integration for personalized diets

Traditional “one-size-fits-all” dietary advice fails to consider differences in genetics, metabolism, and microbiome composition. This results in highly heterogeneous intervention effects¹⁹. Multi-omics studies have confirmed that diet, genetics, and the microbiome account for 9.3, 3.3, and 12.8%, respectively, of variations in plasma metabolites at an individual level²⁰. This suggests the need for personalized modeling. AI can construct precise nutrition prediction models by integrating genomic, metabolomic, microbiome, and dietary log data. These models can predict food metabolic responses in real time based on continuous glucose monitoring^21,22.

The source of data is an important issue, and health tracking can be achieved using wearable data-collecting devices. Traditionally, there has been a lack of real-time linkage between dietary intake and metabolic outcomes. By integrating data such as step counts, heart rate, and sleep patterns from devices like Fitbit and Apple Watch, AI models can correlate physiological responses with dietary intake, thereby predicting the immediate impact of specific foods on blood glucose or lipid levels²³.

The government needs to establish unified data format standards to ensure seamless integration of data from different sources and safeguard consumer privacy and security²³. The government, acting as both a “regulator” and the “rule-maker,” enables technology companies to serve as “technology enablers”. Through legal constraints, technical safeguards, and third-party oversight, it minimizes data risks while maximizing its value for large-scale dietary improvements.

The DayTwo Company uses machine learning to analyze microbiome data and provide personalized dietary advice to help diabetic patients effectively lower their postprandial blood sugar levels. However, multi-omics integration has significant challenges. The differences in temporal resolution and the formatting of different omics data (e.g., second-level blood glucose monitoring versus single-genome sequencing) significantly increase the complexity of modeling²⁴. Furthermore, models tend to prioritize data from high-income countries (e.g., Europe), which results in reduced predictive performance in Africa and South Asia²⁵.

Real-time monitoring and adaptive feedback

Traditional static dietary recommendations, such as fixed recipes, result in delayed intervention and low compliance because they fail to consider metabolic fluctuations caused by circadian rhythms, exercise, and stress^26,27. AI-driven, real-time systems integrate wearable devices (e.g., continuous blood glucose monitors and smart bracelets), environmental sensors (e.g., GPS and image recognition technology), and behavioral logs, providing dynamic interventions^28,29. Reinforcement learning models can adjust dietary suggestions in real time based on feedback such as postprandial blood glucose changes, forming a “monitoring-intervention-optimization” closed loop^9,30.

However, promoting and applying such a system raises issues of data privacy and algorithm generalizations. Sensitive health data collected by wearable devices could be misused by third parties, e.g., to trigger insurance discrimination. There is therefore a strong need to develop a federated learning framework that complies with the General Data Protection Regulation (GDPR), an important piece of EU legislation that enforces strict data privacy and protection measures for individuals. The GDPR specifically governs the processing of sensitive personal data, such as health metrics, and mandates explicit consent, data minimization, and robust security protocols to prevent misuse³¹. Existing models perform poorly with highly heterogeneous populations, such as pregnant women and the elderly. This issue must be addressed through the development of transferable learning algorithms^32,33.

Scaling accuracy in public health

Traditional public health nutrition interventions, such as universal iodine supplementation, often fail to consider population heterogeneity and regional differences³⁴. This makes them ineffective. AI enables precise, large-scale interventions through population stratification and dynamic resource allocation. Integrating geographical, socio-economic, and biomarker data using unsupervised learning algorithms, e.g., K-means clustering and hierarchical clustering. K-means clustering is an algorithm that partitions data into a predefined number of distinct groups (clusters) where each data point belongs to the cluster with the nearest average value (centroid), grouping similar data points together. Hierarchical clustering, on the other hand, builds a tree-like hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or splitting larger clusters into smaller ones (divisive), leading to nested relationships between groups. These algorithms enable high-risk subgroups to be accurately identified. Examples include iron deficiency hotspots among pregnant women and stunted growth in children^35,36.

Agent-based modeling (ABM) is a computational simulation method that focuses on modeling the actions and interactions of autonomous “agents”—such as individuals, households, or institutions within a specific system. These agents act based on predefined rules and characteristics, allowing researchers to observe how collective patterns emerge from individual behaviors. ABM can predict the impact of different food access policies on the vegetable consumption of specific groups, as was used with low-income Latino residents in Austin, Texas, USA³⁷.

Combining AI with ABM enables dynamic forecasting of complex dietary systems. After AI monitors in real time and generates personalized intervention recommendations, the continuously updated user data serves as state input for agents within the ABM. The ABM simulates “future evolution” with current environmental and policy conditions, predicting “how users’ health trajectories would unfold if interventions remained unchanged”, and dynamically adjusts intervention recommendations accordingly. For example, leveraging AI technology to monitor food price fluctuations in real time and dynamically adjust the purchasing decision rules of agents within the ABM to achieve “real-time policy evaluation”³⁸. This approach significantly enhances the dynamic predictive accuracy of complex dietary and health systems, improves the ability to characterize individual heterogeneity, and strengthens real-time policy evaluation capabilities. It enables a closed-loop optimization process spanning from data sensing and personalized interventions to future evolutionary simulations.

However, strictly protecting user privacy remains an important challenge, and the technical costs and complexity involved are also factors that need to be weighed³⁸. Future research must strike a balance between data-driven and mechanism-driven approaches, precision and interpretability, and innovation performance and ethical compliance, developing lightweight, explainable, and privacy-friendly AI-ABM frameworks.

Meanwhile, large-scale progress in precise public health is still hindered by limited data accessibility and policy inertia. Low-income countries lack high-resolution health data, such as village-level nutrition surveys, which reduce model accuracy^39,40. Traditional fixed budget allocation models are also difficult to adapt to dynamic resource scheduling requirements driven by AI⁴¹.

AI-optimized climate-adaptive supply chains

Predictive agriculture and sustainable procurement

Climate change is rapidly altering the conditions that agricultural production relies on. This impact is continuously amplified by the loop between resource availability and the climate system (see Fig. 2). The static decision-making model of traditional agriculture, which relies on historical climate data, is struggling to cope with frequent extreme weather events and the shifting of suitable crop areas. For example, India’s main wheat-producing regions saw a 15% drop in production earlier than expected in 2022 due to an unprecedented heatwave, highlighting the vulnerability of traditional planting planning. However, emerging technologies are increasing system resilience. Deep learning models based on satellite remote sensing and ocean circulation data can, for example, predict regional drought and flood risks weeks in advance and generate visual disaster maps. Multimodal early warning systems combined with large language models can show risk information to vulnerable groups, such as the visually impaired, via voice and text⁴². For breeding, intelligent algorithms can identify high-quality germplasm with robust stress resistance by analyzing the climate adaptability characteristics of global germplasm resource banks. This can significantly shorten the breeding cycle⁴³. Nevertheless, the adoption of these technologies is hindered by practical constraints: meteorological monitoring stations are sparse in low-income areas, and data openness is low. Both factors directly affect prediction accuracy. Small-scale farmers generally lack the skills to operate smart devices. The weak digital infrastructure in rural Malaysia, for example, has impeded the adoption of technology^44,45. Moreover, a reinforcing feedback loop exists between AI’s resource-intensive nature and the climate-food system: greater AI use results in greater resource use (e.g., energy and water). This results in greater environmental damage/climate change, which worsens food insecurity, leading to a heightened need for AI integration, which then requires greater resource use. The fatal flaw of this cycle lies in its explicit “runaway tipping point,” i.e., when AI-driven resource consumption breaches the threshold of environmental carrying capacity (such as groundwater over-extraction reaching irreversible levels or greenhouse gas emissions triggering irreversible changes in the climate system), the entire feedback loop will shift from a “regulatable dynamic equilibrium” to an “irreversible vicious spiral”. At this point, the speed of environmental degradation will far outstrip the speed at which AI can optimize food production. Not only will AI fail to resolve food security issues, but the runaway feedback loop may trigger a dual collapse of both the global food system and the climate system. Therefore, to prevent the use of AI resources from forming an irreversible vicious feedback loop with the climate-food system, it is necessary to establish proactive constraint mechanisms at the levels of technological design, resource management, and global governance. This will safeguard ecological carrying capacity thresholds and drive AI toward a sustainable paradigm characterized by low carbon emissions, water conservation, and enhanced efficiency.

Fig. 2. Regional water-energy-food nexus framework for the southern African development community⁸⁰.

This figure focuses on the interrelationships among water, energy, and food security, with urbanization, population growth, and climate change as the core drivers, and aims to achieve regional sustainable development goals through collaborative governance and six key areas of action.

Dynamic logistics and waste reduction management

Globally, ~1.3 billion tonnes of food are lost each year during distribution, primarily due to static inventory and fixed transportation routes^46,47. Improvement might include establishing an interconnected supply chain system that enables full-chain data traceability and dynamic responses⁴⁸. Case studies have shown that analyzing consumption trends to adjust inventory in real time and using autonomous vehicles to optimize delivery routes can reduce food waste-related emissions by 30% in one retail enterprise’s pilot store. Further research has confirmed that intelligent algorithms integrating multi-objective optimization can ensure the quality of fresh produce while reducing transportation costs and carbon emissions⁴⁹. However, there are still important obstacles to cross-linked collaboration. Production, logistics, and retail data are difficult to share due to formatting and permission issues, creating “data silos”. Real-time path optimization also requires substantial computing power for edge computing devices, which limits its large-scale application^50,51.

Circular economy and upgraded recycling of by-products

Of the 1.3 billion tonnes of by-products generated by the food processing industry each year, many valuable components remain underutilized⁵². AI-driven upcycling technology can convert these by-products into higher-value products by extracting their valuable components and optimizing the processes⁵³. For example, artificial intelligence and machine learning can analyze complex data on substrate types, enzyme and microbial characteristics, and variable processing parameters, to identify the most suitable extraction conditions. AI-guided optimization of the biological process for recovering functional compounds from plant waste utilizes the reliability of these processes to maximize yield while minimizing resource waste and environmental impact. AI can monitor and adjust extraction conditions in real time, as well as model and precisely simulate the extraction process. This improves understanding and efficiency⁵⁴. However, the promotion of this technology is constrained by data heterogeneity and economic feasibility. The composition of by-products can vary significantly depending on the source of the raw materials (e.g., different types of wheat bran from different strains grown in different places at different times), so a cross-species database needs to be created to support the necessary modeling. The production cost of many upgraded circular products still generally exceeds that of petrochemical-based alternatives (e.g., synthetic dietary fiber), so policy incentives (e.g., a carbon incentive) may be required to offset the initial investment⁵².

Synergy and trade-offs: balancing personalization and sustainability

Collaborative paths for personalization and sustainability

The traditional food system often sets personalized nutrition against the sustainability of the supply chain. For example, consumers’ demand for a variety of ingredients can require high-carbon generating, long-distance transportation. People are increasingly interested in specific health foods, as well as sustainable innovations in agricultural food systems⁵⁵. However, emerging research suggests that a data loop could optimize and balance the two, thereby effectively supporting the R&D, production, and efficient supply of specific products. AI’s basic contribution to personalization of food systems is primarily reflected in demand-side precision nutrition customization and localized guidance, i.e., recommending local, seasonal ingredients based on individual metabolic characteristics could reduce the carbon footprint of imported, out-of-season produce.

AI can address the root causes of overproduction and resource waste in traditional supply chains through personalized demand response and capacity optimization on the supply side. These issues stem from the delayed perception of fragmented, personalized demand. AI’s dynamic demand forecasting models integrate vast amounts of individual nutritional data to accurately predict ingredient demand structures for different regions and demographics—such as the proportion of high-fiber ingredients required in a specific community or peak demand for calcium-enriched foods for a particular age group (see Fig. 3).

Fig. 3. AI dual-loop collaborative framework diagram.

This figure illustrates the synergistic optimization of nutrition and health with the supply chain through a three-tiered integrated approach. 1. With AI technology at its core, the system achieves synergistic optimization between consumers and agricultural producers through precise demand prediction and localized ingredient recommendation. 2. AI breaks down data silos, dynamically linking the nutrition and health sector with the food supply chain to create a closed-loop system with two-way interaction. 3. Through dynamic policy adjustments involving carbon taxes and nutrition subsidies, we aim to achieve a multi-objective balance among economic benefits, carbon emissions reductions, and nutritional health, ultimately driving the sustainable transformation of the entire food system.

A fresh food delivery service in the Netherlands, for example, has significantly reduced its logistics emissions by optimizing delivery routes and order timing. Tokyo’s nutrition intervention project increased the purchase rate of regional vegetables by 25% by integrating residents’ health data with local farm production capacity and promoting the “local production, local consumption” model. For individuals managing their sugar intake, AI not only precisely calculates daily carbohydrate intake thresholds but also prioritizes recommendations for locally sourced, seasonal low-GI ingredients (such as seasonal whole grains, fruits, and vegetables) over off-season ingredients requiring transcontinental shipping⁵⁶. Nevertheless, important obstacles to this type of collaboration remain: communicating privacy-related nutrition data alongside commercially sensitive supply chain data is difficult⁵⁷, and existing policies (such as carbon taxes and nutrition subsidies) have not yet been integrated into the dynamic optimization mechanism⁵⁸.

Practical challenges to ethical governance

The large-scale application of AI in the food system raises ethical issues, such as data monopolies and algorithmic bias. A few enterprises have become ‘digital oligarchs’ by controlling users’ health data, including records from wearable devices and diet logs. This leads to an imbalance in resource allocation. For example, certain global nutrition AI platforms prioritize serving high-income users due to their commercial focus, making it difficult for low-income groups to access personalized dietary advice. To strike a balance between innovation and fairness, a cross-border governance framework should be established. This can draw on the EU’s “GAIA-X” project to safeguard user data sovereignty and on the United Nations Climate Group’s model to set up an international ethics committee comprising multiple entities that can create inclusive standards. However, governance practices face a dual challenge: the speed of technological iteration (calculated monthly) far exceeds the policy adjustment cycle (calculated annually), resulting in delayed supervision. Cultural differences in the perception of “fairness” also hinder the formation of a global consensus. For example, the emphasis placed on individual rights in Europe and America contrasts with the focus on community well-being in Africa⁵⁹.

Case study: empowering the practical transformation of food systems through technology

In response to the challenges of geopolitical and climate-related issues, the Republic of Singapore, a nation with a food import dependence rate exceeding 90%, has initiated the “30×30” strategy. The objective of this initiative is to achieve 30% of its nutritional requirements through the utilization of smart agricultural technologies by the year 2030^60,61. This strategy achieves an upgrade from group-based nutritional provision to personalized precision nutrition through a closed-loop system encompassing “nutritional profiling—AI-driven precision regulation—on-demand production—trustworthy traceability”. By building personalized nutritional requirement models based on a consumer’s genetic data, gut microbiome profiles, and health objectives, AI reverse-engineers vertical farming systems to optimize environmental parameters and selectively enrich for target nutrients. Blockchain technology records nutritional composition and production processes, ensuring traceability and credibility for customized products.

The program’s fundamental activities are as follows: AI achieves precise management of vertical farm environments and crop growth by establishing a closed-loop control system. Based on multi-source sensor data, including temperature, humidity, light intensity, CO₂ concentration, nutrient solution parameters, and crop phenotype images, the AI model can analyze growth conditions in real time. Through reinforcement learning and deep learning, it dynamically optimizes environmental parameters to achieve precise matching of photoperiod, light spectrum, and water-fertilizer supply, thereby significantly enhancing resource utilization efficiency and yield per unit area. Meanwhile, AI vision technology is widely applied in pest and disease identification, growth monitoring, and ripeness prediction. Combined with automated robots to handle tasks such as seeding, transplanting, and harvesting, it allows vertical farms to reduce staffing and optimize unmanned operations. AI also participates in crop variety selection and nutritional formulation optimization. By integrating genetic, phenotypic, and environmental data, it enhances the adaptability of local crops to high-density, low-energy production models, further strengthening the sustainability of local production. Furthermore, these farms use a dynamic import model that has the capability to activate alternative solutions within 72 h in the event of a disruption to the supply chain. Singapore has become a global benchmark for vertical farming development. Driven by extreme land scarcity and stringent food self-sufficiency policies, its vertical farming industry has achieved a high degree of maturity in technology (hydroponics/aeroponics/substrate cultivation), commercialization, and policy support. The industrial development model serves as a global benchmark, with relevant data reflecting the current state of large-scale vertical farming operations⁶². A particularly salient example of this is evidenced by the disruption to the chicken supply chain in Malaysia in 2022. However, this strategy is constrained by high costs. Vertical farms invest 50 times more than traditional agriculture and account for 1.2% of the national electricity consumption, which may not meet the need for a low-carbon transformation.

At the enterprise level, the IBM Food Trust platform uses blockchain to trace the entire process data from “farm to shelf” (including carbon emissions), increasing the credibility of sustainability claims⁶³. The IBM Food Trust data warehouse design adheres to industry standards for the food supply chain. Its data dimensions encompass entities (enterprises, logistics providers, and end consumers), time (real-time, and daily/weekly/monthly summaries), and metrics (quantitative and qualitative). The granularity supports both micro-level enterprise case analysis and macro-level industry trend analysis, meeting data requirements for research methodologies such as statistical analysis and model building⁶⁴. As an enterprise-level implementation platform, IBM Food Trust data originates from real commercial operations rather than theoretical research, authentically reflecting the practical application outcomes, pain points, and optimization directions of digital technologies within the food supply chain. Its research conclusions directly align with industry practices, offering exceptional reference values for implementation.

The specific strategy for this platform is as follows: AI cleans, standardizes, and validates heterogeneous data from IoT, RFID, logistics, and other sources to ensure the quality of data uploaded to the blockchain. Thus, achieving batch-level traceability and precise recalls within seconds through knowledge graphs. The platform can perform dynamic accounting of Scope 1–3 carbon emissions based on full lifecycle data, generate regulatory-compliant carbon reports, and identify emission reduction pathways. It can detect anomalies in time-series data for cold chain and logistics operations, combine with NLP to monitor external risks, and trigger smart contract alerts and scheduling. And it may collaborate with smart contracts to automate processes such as quality verification, compliance checks, and settlement payments. Nestle has successfully reduced the sodium and total sugar content in its products by 22–31% based on their nutrition target algorithm⁶⁵, and its generative technology-based plant-based dairy product “Wunda” reduces blood sugar, increases protein, and reduces carbon intensity simultaneously.

For formula design, AI balances nutritional constraints, sensory characteristics, and process feasibility. It uses taste models and alternative ingredient recommendations to compensate for flavor changes resulting from salt and sugar reduction. Simultaneously, it can do intelligent tiering and risk screening across global product lines, translating nutritional objectives into actionable formulations and production parameters. This enables scalable, systematic health-focused improvements, providing a technological paradigm for sustainable nutritional transformations in the food industry. For small and medium-sized enterprises, the Agri-Food Protection Alliance has established a unified AI knowledge platform.

This platform integrates member companies’ customer preferences, market data, and economic environment information, enabling algorithms to achieve precise matching between “enterprise needs” and the “alliance knowledge base”. For example, it pushes the target country’s food standards and consumer habits data to help export companies⁶⁶. Although this technology uses AI for precise knowledge matching and demand forecasting, it does not incorporate a blockchain system. There are still obstacles to cross-subject collaboration: small and medium-sized farmers find it difficult to access blockchain systems, leading to the breakage of data chains⁶⁷, and consumers’ concerns about the transparency of technology-based foods also need to be addressed. These examples show that technological empowerment needs to simultaneously address the issues of cost allocation and trust building.

Future direction: breakthrough paths for hyper-personalized and adaptive systems

The prevailing food system modeling has dual bottlenecks of exponentially increasing computational complexity and excessively high energy consumption. The integration of quantum computing offers a new approach to overcoming this situation: quantum algorithms have been shown to efficiently optimize supply chain problems involving millions of variables⁶⁸. Furthermore, quantum neural networks have been shown to simulate nutritional metabolic processes at the molecular scale, including food-microbial interactions. Its potential has been confirmed in the life sciences⁶⁹. However, there are still important obstacles to its practical application: the scale of quantum hardware (only 50–100 qubits) has difficulty supporting the modeling of complex systems, and there is a severe shortage of workers proficient in quantum technology, nutrition, and supply chain⁷⁰.

The intricate nature of the food system necessitates collaborative data management among government entities, industry, and academic institutions. However, the prevalence of data silos remains a significant challenge to the development of effective solutions^71,72. Typical cases include the EU’s “Farm2Fork” platform, which achieves secure data sharing using blockchain⁷³, ensuring data sovereignty while supporting precise decision-making⁷⁴. Nevertheless, there remain profound and entrenched contradictions that have yet to be addressed. The process of anonymization has been shown to compromise data integrity⁷⁵. Furthermore, public platforms have an excessive reliance on government funds, whilst concurrently having insufficient corporate participation. This hinders the development of a sustainable collaborative ecosystem.

At the same time, the widespread adoption of AI may exacerbate imbalances in the global food system. On one hand, AI technology and ownership are concentrated in large corporations, enabling them to more completely control food supply chains and intensify economic and social inequalities⁷⁶. On the other hand, training data is monopolized by large corporations, which can easily abuse their market dominance to squeeze out small farms and enterprises, thereby exacerbating land monopolization and resource misallocation⁷⁷. Therefore, beyond corporate self-regulation, it is necessary to establish an interdisciplinary ethical governance framework that clarifies primary responsibilities and collaborative mechanisms, thereby providing guidance for both oversight and practical implementation. At the same time, the government should establish a systematic and mandatory policy framework that balances theoretical rigor with practical applicability⁷⁸. Therefore, there is a need to incorporate food and nutrition AI into public interest regulations to ensure the algorithms serve public health. Antitrust enforcement of targeting data, algorithms, and supply chains needs to be strengthened, while promoting the opening of public data. Another need is to establish a mechanism for algorithmic record-keeping, third-party audits, and accountability, and prohibiting the promotion of unhealthy food products to vulnerable groups⁷⁹. The government should be establishing a public food AI platform, benefiting smallholder farmers and public health institutions. Therefore, overcoming the hardware limitations and the elimination of collaboration bottlenecks have become necessary for the development of next-generation systems.

Conclusions

AI offers a new approach to reconciling nutritional health and supply chain sustainability within the food system. Precision nutrition, as a basic application scenario for AI, integrates multi-omics data, including genomics, metabolomics, and gut microbiomics. By combining individual dietary preferences and needs, lifestyle habits, and real-time physiological monitoring data, it leverages machine learning algorithms to construct personalized nutritional intervention models. This significantly enhances the accuracy and specificity of intervention measures, effectively overcoming the limitations of traditional one-size-fits-all nutritional advice. Clinical studies indicate that this type of model can reduce postprandial blood glucose levels by an average of 21%. It also has a significant role in the prevention and auxiliary intervention of nutrition-related chronic diseases such as obesity and diabetes, providing a new technological foundation for enhancing the nutritional health of the entire population.

In terms of supply chain resilience, AI-based climate prediction models can accurately forecast the impact of extreme weather and climate change on crop cultivation, harvesting, and storage. Dynamic logistics optimization algorithms enable the most efficient routes and minimal losses for food from production to consumption. Combined with real-time inventory monitoring and demand forecasting, this significantly enhances the stability and efficiency of the food supply chain. Practical data has shown that the large-scale application of this technology has reduced carbon emissions across the entire food supply chain by 15–30%.

Among these efforts, Singapore’s “30 × 30” strategy has seen the deep integration of AI technology into environmental control, crop cultivation, and yield forecasting within vertical farming. This has successfully boosted the nation’s food self-sufficiency rate, providing a replicable model for the sustainable development of urban food systems. It shows the practical value and application potential of AI in optimizing food supply chains.

However, there are some significant gaps in the current research. For example, data silos containing nutrition data and supply chain logs in different formats and with different permissions make modeling the entire chain difficult. The absence of algorithmic ethics: there is insufficient interpretability and fairness, and regions with limited resources are facing “data colonialism” and technological gaps. Policy lag: Dynamic optimization mechanisms, such as carbon labeling and nutrition subsidies, have yet to be incorporated into the policy framework. Future research should focus on the following aspects:

1. Technology integration: Focusing on modeling and optimizing the needs for hyperscale food systems (such as global food supply chains and nationwide precision nutrition interventions), develop hybrid algorithms integrating quantum computing and AI that balance computational efficiency with energy consumption control. Processing the large and complex data within food systems in a more energy-efficient and effective manner, enhancing model accuracy and generalization capabilities, overcoming computational bottlenecks in traditional algorithms for end-to-end collaborative optimization, and driving the transformation of technology from single-process applications to end-to-end collaborative applications.

2. Data governance: Leveraging the decentralized, immutable, and traceable characteristics of blockchain technology to establish a distributed data sharing and governance platform. Drawing on the development experience of the EU’s GAIA-X data space should allow the creation of data governance rules that balance privacy protection with data utility. Clarifying data ownership, usage rights, and revenue rights will strengthen governance. Technical measures such as data encryption and de-identification will promote the interdisciplinary, cross-sectoral, and cross-border sharing and collaborative utilization of nutrition and health data alongside supply chain data. This approach safeguards individual privacy and corporate data rights while breaking down data silos, thereby providing data support for collaborative modeling across the entire chain.

3. Ethical framework: Promote the establishment of an international AI ethics committee, bringing together research institutions, enterprises, and government departments from various countries to jointly develop transparency standards for AI algorithms in food systems, data sovereignty protection mechanisms, and ethical review protocols. This initiative aims to define clear boundaries and ethical thresholds for AI technology applications within food systems, thereby preventing technological monopolies and mitigating ethical risks. Together, they will ensure the fairness, impartiality, and sustainability of technology applications and promote the responsible use of AI technologies within food systems.

4. Policy innovation: Design a “nutrition-climate” collaborative policy toolkit that incorporates AI technology into the food system development policy framework and improves the dynamic management mechanisms for carbon labeling, nutrition subsidy policies, and green supply chain incentive policies. For example, implementing a dynamic carbon tax system based on AI-driven precision calculations. This might impose differentiated taxation according to the carbon emissions levels of enterprises’ supply chains to guide companies in reducing their carbon emissions. Establishing a regional dietary subsidy linkage mechanism to integrate AI-driven precision nutrition forecasting to optimize the distribution methods and coverage of nutritional subsidies. This will promote the coordinated development of nutritional health and supply chain sustainability, achieving the unified economic, social, and environmental benefits of an optimized food system.

AI statement for the preparation of the manuscript

The authors certify that generative AI (i.e., software that can write text) was not used in preparing this paper. However, the spelling and grammar checkers in Word and a references management software were used, and the references were checked for consistency thereafter. Other software used to prepare the manuscript is noted in the text or figures/tables.

Also, note that the authors do not give their permission for this article to be used for AI training.

Author contributions

L.Z.: Conceptualization, Methodology, Writing—original draft, Writing—Reviewing and editing, Supervision. A.R. and Y.S.: Visualization. Y.X. and H.T.: Investigation. Z.W.: Resources. J.M.: Writing—Reviewing and editing. Project administration. All authors have read and agreed to the published version of the paper.

Data availability

No datasets were generated or analyzed during the current study.

Competing interests

The authors declare no competing interests.