Abstract
The human microbiome is integral to health, metabolism, immunity, and physical protection, with dysbiosis linked to metabolic disorders, autoimmune conditions, and cancer. High-throughput sequencing has generated massive datasets, but analyzing these complex, multidimensional data poses significant challenges. Artificial intelligence (AI), including machine learning, deep learning, and related approaches, offers powerful solutions for data integration, pattern recognition, functional annotation, and predictive modeling. AI applications in microbiome research include identifying microbial biomarkers, predicting disease susceptibility, elucidating microbial interactions, and optimizing personalized interventions. AI also enables integration of microbiome data with host genomics, transcriptomics, and clinical metadata, providing a holistic view of host-microbiome dynamics. Despite advances, challenges such as data heterogeneity, model interpretability, and limited sample sizes remain. This narrative review synthesizes literature from 2015 to 2025 without a formal systematic methodology. We conclude with three priority areas for AI-microbiome translation: improved interpretability, longitudinal modeling, and clinical integration. Leveraging AI in this field holds significant promise for accelerating precision medicine, improving diagnostics, and developing microbiome-targeted therapies, if interpretability and validation challenges are addressed.