Diabetes is becoming increasingly widespread across the globe, thereby highlighting the need to ensure timely and precise prediction to avert serious complications. This paper proposes a comparative and explainable ensemble machine learning framework to predict early diabetes diagnosis from publicly available Kaggle clinical dataset of 2,000 patients with eight clinical features. Data was preprocessed using feature scaling, median imputation and handling of invalid zero value. The models were evaluated using an 80:20 train-test split with stratification, and 5-fold stratified cross-validation was employed as a means of evaluating the robustness of the models. Ensemble models like Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost were also compared with baseline classifiers such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, Decision Tree, and Support Vector Machine. Model performance was evaluated by using accuracy, precision, recall, F1-score, ROC-AUC, balanced accuracy, MCC, Kappa score and specificity. Random Forest had the highest performance among all the models, with an accuracy of 98.50% and an AUC of 0.9971 on the test set. SHAP and LIME were used to provide global and instance-level explanations, where Glucose, BMI, and Age were always found to be significant predictive variables. The suggested framework is highly predictive and easy to interpret and exhibits its possibility to support reliable and transparent clinical decision-making in the early stages.