Diabetes Mellitus is a significant world health and early detection is of paramount significance since it decreases the complications and enables medical intervention in time. The paper is a comparison between the predictive accuracy of the eight Machine Learning classifiers: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, Naive Bayes, k-Nearest Neighbors (k-NN), and an Ensemble model on the Pima Indian Diabetes dataset and a collection of clinical-biological patient records. Performance evaluation was conducted using Precision, Recall, F1-Score, and the Area Under the ROC Curve (AUC-ROC). The findings show that a significant difference was observed among the models, with SVM (AUC-ROC: 0.8648) and the Logistic Regression (AUC-ROC: 0.8638) having the best discriminative ability. A comparable study found that Logistic Regression had the highest Precision (0.7632), indicating fewer false-positive predictions, whereas Decision Tree had the highest Recall (0.7447), indicating greater sensitivity in detecting diabetes cases. The ensemble learning produced the best overall performance (AUC-ROC: 0.8709), suggesting that combining predictions from multiple models increases reliability and generalization. On the other hand, k-NN performed worst due to sensitivity to noise and the number of features. In general, the results provide evidence of the high potential of linear-margin and ensemble-based models to structured clinical data and would be a robust foundation of clinical decision support systems, which further help to broaden the role of ML-based analytics in early diabetes diagnosis and preventive health care planning.