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.
Zheng et al. (2026) studied this question.