Artificial Intelligence Based Personalized Nutrition Systems for Optimizing Dietary Recommendations and Health Outcomes

Abstract

The growing burden of nutrition-related chronic diseases, including obesity, type 2 diabetes, cardiovascular disorders, and metabolic syndrome, has exposed the limitations of conventional population-based dietary recommendations and accelerated the transition toward precision nutrition. Advances in high-throughput omics technologies and artificial intelligence (AI) have enabled the development of personalized nutritional frameworks capable of integrating genomic, epigenomic, metagenomic, proteomic, metabolomic, behavioral, and lifestyle data to predict individualized metabolic responses. This review synthesizes current evidence on the biological foundations, computational architectures, clinical validation, and translational applications of AI-driven personalized nutrition. A systematic examination of studies published between 2010 and 2025 highlights the increasing use of machine learning, deep learning, graph neural networks, transformers, computer vision systems, and large language models for dietary assessment, glycemic prediction, behavioral modeling, and malnutrition screening. Landmark intervention studies, including PREDICT, DIETFITS, Food4Me, and the ongoing Nutrition for Precision Health initiative, demonstrate substantial inter-individual variability in postprandial responses and emphasize the importance of gut microbiome composition and lifestyle factors beyond genetic determinants alone. The review further explores commercial implementations, continuous biometric monitoring, Internet of Things integration, and applications across food manufacturing and supply chains. Despite significant advances, major challenges remain regarding algorithm interpretability, data privacy, regulatory compliance, demographic bias, accessibility, and health equity. Emerging privacy-preserving approaches, including federated learning, differential privacy, and homomorphic encryption, offer promising pathways for secure and ethical deployment. Overall, AI-enabled precision nutrition represents a paradigm shift from generalized dietary recommendations toward dynamic, data-driven, and individualized interventions. Continued integration of explainable AI, diverse population datasets, and clinically validated models will be essential to realizing the full potential of personalized nutrition in improving metabolic health and reducing the global burden of chronic disease.