Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) arises from complex interactions among host genetics, immune dysregulation, environmental factors, and the gut microbiome. Numerous studies have demonstrated alterations in microbial composition and function, including reduced diversity and changes in metabolic pathways. Traditional biostatistical approaches, such as differential abundance analysis, have advanced our understanding but are remain limited in handling nonlinear and high-dimensional data. Machine learning (ML) complements these methods by integrating heterogeneous datasets and uncovering hidden patterns that improve classification and predictive accuracy. In IBD, delayed diagnosis and the lack of reliable biomarkers highlight the need for computational tools that can translate complex microbiome data into clinically actionable insights. ML and deep learning (DL) have expanded analytical capabilities, enabling disease classification, subtype differentiation, and prediction of therapeutic responses. This review provides an integrative perspective on how ML and DL are reshaping microbiome-based IBD research, summarizing their strengths, limitations, and essential considerations for clinical translation. Future progress will depend on standardized microbiome assays, rigorous benchmarking, and the integration of multi-omics data to elucidate host-microbe interactions. With these advancements, ML- and DL-based approaches may offer precise diagnostics and personalized treatment strategies, transforming microbiome research into practical tools for IBD care.