Substance use disorders (SUDs) and inflammatory bowel disease (IBD) are increasingly linked to gut microbiota alterations, which influence metabolism, immune function, and neurological health. This study employs advanced computational bioinformatics techniques, including statistical validation, clustering algorithms, and dimensionality reduction methods, to analyze gut microbiota composition in individuals with alcohol use disorder (AUD), opioid use disorder (OUD), cannabis use disorder (CUD), and IBD. Using 16S rRNA sequencing open-source raw data, we assessed microbial shifts across groups. Firmicutes and Bacteroidota emerged as the dominant phyla, with significant variations: the CUD group showed a higher Bacteroidota proportion, while IBD displayed increased Firmicutes representation. The AUD group formed a distinct microbiota cluster, suggesting unique alterations. Principal Component Analysis (PCA) and hierarchical clustering revealed overlapping microbial profiles among SUD and IBD groups, supporting the hypothesis of shared dysbiosis patterns. Effect size calculations and machine learning-based classification models identified key phyla contributing to microbial imbalances. The computational integration of microbiome data enabled the identification of distinct clustering patterns and demonstrated the potential for predictive modeling in microbiota-based diagnostics. These findings underscore the power of bioinformatics in deciphering gut microbiota complexities in SUDs and IBD, offering insights for precision medicine applications and microbiome-targeted therapeutic interventions.