Diabetes mellitus is a major chronic metabolic disorder whose increasing prevalence has created a need for effective approaches to early risk identification and prevention. The rapid growth of electronic health records, population health surveys, and patient-generated health information has created opportunities for data-driven approaches to diabetes risk prediction. Machine learning can analyze large numbers of demographic, behavioral, and clinical variables to identify patterns associated with diabetes risk. Among these approaches, ensemble learning has demonstrated considerable potential because it combines multiple predictive models to improve robustness, generalization, and classification performance. This article examines the application of advanced ensemble learning methods for data-driven diabetes risk prediction, with particular attention to Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, voting, and stacking approaches. The proposed framework incorporates data preprocessing, feature selection, class-imbalance management, ensemble model development, hyperparameter optimization, and explainable artificial intelligence. Recent research indicates that advanced ensemble approaches can achieve strong predictive performance, although reported results vary considerably according to dataset characteristics, population, feature engineering, and validation strategy. A recent study using 253,680 observations from the Behavioral Risk Factor Surveillance System demonstrated the potential of combining XGBoost, Random Forest, CatBoost, LightGBM, and K-Nearest Neighbors for diabetes prediction after addressing class imbalance with SMOTE-ENN. The article argues that reliable diabetes risk prediction requires not only high predictive performance but also rigorous validation, interpretability, population diversity, and careful consideration of data quality.