Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.

Diet-gut microbiome interactions drive substantial inter-individual variability in metabolic responses to food, a fact that challenges the efficacy of uniform dietary recommendations. To address this complexity, advances in multi-omics profiling, dietary assessment technologies, and host clinical phenotyping now generate high-resolution multimodal datasets. However, managing these vast amounts of data necessitates the integration of artificial intelligence (AI) and machine learning (ML) approaches. In this review, we first delineate the multimodal data landscape and its associated computational workflows. These range from the initial preprocessing of heterogeneous inputs (filtering, normalization, dimensionality reduction) to ML modeling strategies designed to address high dimensionality, sparsity, and compositionality through feature engineering and regularization. We then summarize core ML applications, including the classification of habitual dietary patterns from microbiome signatures, prediction of postprandial metabolic responses, responder stratification, and in silico simulation of dietary perturbations. Furthermore, recent randomized controlled trials demonstrate the tangible clinical potential of AI-guided personalization. Next, we highlight composite microbiome health metrics and diet-specific indices, such as GMWI2 and DI-GM. These tools are essential because they condense high-dimensional taxonomic profiles into interpretable wellness scores for monitoring diet-induced shifts. We subsequently examine genome-scale metabolic models and microbiome "digital twins" that mechanistically link dietary substrates to community metabolism and host-relevant metabolites. We also discuss emerging hybrid AI-mechanistic frameworks that enhance interpretability, biological plausibility, and scalability. Finally, we outline translational priorities-including the development of diverse longitudinal cohorts, standardized benchmarking, and clinically trustworthy AI-that are required to realize equitable, microbiome-informed precision nutrition.

© 2026 The Author(s). Published with license by Taylor & Francis Group, LLC.