Early and accurate identification of individuals at risk of diabetes improves clinical outcomes and reduces healthcare burden. This paper evaluates classical supervised learning methods—Logistic Regression, Decision Tree, K-Nearest Neighbors, and Random Forest—on a clinically oriented dataset containing demographic, lifestyle, comorbidity, and biochemical features (BMI, HbA1c, blood glucose, hypertension, heart disease, age, gender, and smoking history). We present a reproducible preprocessing pipeline (median imputation, outlier clipping, categorical encoding, and standard scaling), five-fold cross-validated hyperparameter tuning, and a comprehensive ablation protocol centered on Random Forest. The ablation results show that HbA1c and blood glucose are the most informative predictors: removing both reduces test accuracy from 91.16% to 71.54%. We discuss feature-scaling rationale, optimization choices (grid search, class weighting), robustness improvements (stratified sampling, ensemble averaging), and statistical validation considerations. High-resolution figures for feature importance and confusion analysis are included. The study provides actionable recommendations for prioritizing clinical measurements, integrating explainable AI techniques, and extending to longitudinal and multi-site validation for real-world deployment.