Artificial Intelligence in Food–Nutrition–Health Research: From Multimodal Data Integration to Precision Intervention

Artificial intelligence (AI) is transforming food–nutrition–health research by enabling pattern recognition in complex, high‐dimensional datasets that traditional hypothesis‐driven approaches cannot address. This review systematically synthesizes research progress of AI across the food–nutrition–health continuum from 2020 to 2025. By examining 181 systematic reviews through PRISMA‐guided selection, we provide a comprehensive overview and prospects across four dimensions: technical foundation, application scenarios, existing challenges, and future prospects. We propose a tripartite framework comprising (1) a data layer enabling multisource fusion of food composition, health monitoring, and individual characteristic data; (2) a technological layer of nondestructive testing (spectroscopy, nuclear magnetic resonance [NMR], imaging); and (3) an algorithmic layer progressing from machine learning to deep learning architecture. Key applications include food component analysis and safety detection; nutrition–disease association modeling; pathogen identification; and personalized dietary intervention systems. Despite rapid progress, critical challenges persist, insufficient model generalization across populations, algorithmic opacity limiting clinical trust, data privacy vulnerabilities, and lack of standardized multi‐omics integration protocols. Future directions emphasize multimodal fusion models, explainable artificial intelligence (XAI), federated learning for privacy‐preserving collaboration, gene‐guided precision nutrition, and development of intelligent wearable devices and functional food. This review provides a roadmap for transitioning from population‐averaged guidelines to dynamic, individualized health optimization through AI‐enabled food system.