Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration

= 0.91 in complex protein digestibility matrices. Finally, we demonstrate how Microbial Community-scale Metabolic Modeling (MCMM) and multimodal machine learning mechanistically couple specific dietary inputs with unique microbiome responses, yielding up to 95% diagnostic accuracy in differentiating diet-responsive metabolic states and individualized postprandial glycemic outcomes. We also emphasize the importance of Graph Neural Networks (GNNs) in rationally designing bioactive peptides, and Artificial Neural Networks (ANNs) for the optimization of microencapsulation, which have demonstrated the capacity to reduce physical experimental formulation trials by over 60%. Furthermore, we evaluate AI's emerging role in precision aquaculture, where computer vision and predictive models yield 94-99% accuracy in disease detection and reduce feed conversion ratios by up to 11%. This review discusses the need to connect computational intelligence and food function, delineating a path toward autonomous metabolically-aware food ecosystems whilst identifying important research gaps in mechanistic interpretability, data standardization, and cultural bias underpinning precision nutrition.