AI Driven Metabolomics for Precision Nutrition: A Food Science and Technology Perspective

Abstract

The integration of artificial intelligence (AI) and metabolomics is transforming the fields of food science, technology, and nutrition by enabling data-driven, individualized dietary strategies. Personalized nutrition seeks to move beyond generalized dietary recommendations toward precision-based dietary planning informed by genetic, metabolic, microbiome, and lifestyle variability. AI techniques including machine learning, deep learning, and predictive analytics enable the interpretation of high-dimensional biological and dietary datasets, while metabolomics provides comprehensive biochemical profiling that reflects nutrient metabolism, bioavailability, and host–microbiome interactions. From a food science perspective, these technologies facilitate intelligent food formulation, optimization of processing conditions, functional ingredient development, and personalized product design. From a nutrition standpoint, AI-integrated metabolomics enhances prediction of postprandial responses, metabolic risk stratification, and chronic disease prevention strategies. This review critically synthesizes current advances, technological frameworks, translational applications, and challenges associated with AI–metabolomics integration in personalized nutrition.  However, despite these advancements, several challenges remain, including high costs of metabolomic technologies, data privacy concerns, limited interpretability of complex AI models, and the need for large-scale validation across diverse populations. Addressing these limitations will be essential for the successful implementation of AI-driven precision nutrition within clinical and food system applications.  The implications for food engineering, functional food innovation, and regulatory governance are also discussed.