Background <p>Type 1 Diabetes Mellitus (T1D) has been increasingly associated with alterations in the gut microbiome. However, the impact of taxonomic resolution, feature selection strategies, and machine learning methods on microbiome-based prediction remains incompletely understood.</p> Methods <p>We analyzed publicly available 16S rRNA gene sequencing datasets from two geographic cohorts to evaluate microbiome-based prediction of T1D. Microbial features were constructed at multiple taxonomic levels and as full hierarchical taxonomic paths preserving phylogenetic structure. Machine learning models were trained using stratified cross-validation and cross-cohort validation frameworks. Feature selection was performed using Binary Particle Swarm Optimization (BPSO) to identify compact and predictive microbial signatures. Model performance was evaluated using AUC, Accuracy, F1 score, and Matthews Correlation Coefficient. Differential abundance analysis using the LinDA framework was used to support biological interpretation of selected taxa.</p> Results <p>Tree-based models, particularly Random Forest and XGBoost, achieved the strongest predictive performance across taxonomic representations. Taxonomic resolution influenced model behavior, with family-level features providing strong performance with compact feature sets, while higher-resolution representations did not consistently improve performance despite increased complexity. BPSO identified consistently selected taxa across validation frameworks, suggesting stable predictive signatures. Several of these taxa have been linked to inflammatory or metabolically altered gut environments. Cross-cohort validation showed reduced performance compared with within-study models, highlighting challenges in generalization.</p> Conclusion <p>Machine learning combined with BPSO-based feature selection provides an effective framework for identifying predictive microbial signatures associated with T1D. Our findings highlight the importance of taxonomic resolution, feature stability, and cross-cohort validation in microbiome-based predictive modeling. Integrating evolutionary feature selection with machine learning and biological validation may improve the robustness and interpretability of candidate microbial signatures.</p>