Diabetes is a chronic metabolic disorder affecting millions globally, requiring early detection for effective management. Machine learning techniques have emerged as powerful tools for accurate and efficient diabetes prediction. This study applied SHAP analysis and key influencer evaluation to reveal that polyuria, polydipsia, and sudden weight loss are the most significant predictors of diabetes and evaluates four machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree—on a dataset containing key medical indicators. Model performance is assessed using Accuracy, Precision, Recall, F1-score, and Receiver Operating Characteristic analysis. Logistic Regression and SVM exhibit balanced precision and recall, making them reliable classifiers. The Decision Tree model, despite high recall, shows overfitting tendencies. Analysis results indicate that the Random Forest model achieves the highest accuracy (98%)  and F1-score (0.986667)  demonstrating strong capabilities in predicting diabetes in adults.