This research therefore provides an enhanced mathematical approach to the determination of diabetes with new approaches in the machine learning algorithms. The research focuses on, and analyzes the efficacy of, four algorithms, namely Logistic Regression, Random Forest, SVM, and XGBoost on a large data set. By choosing only the most important features and by a proper tuning of the model, it was possible to obtain here 94% of accuracy of the prediction. 2%, outperforming existing models. The Random Forest carried out the best result with accuracy of 96. At the end, we obtain 1% for our submission and XGBoost gives 95. 3%, SVM at 93. 8%, Logistic Regression at 91. 5%. The effectiveness of the presented framework was proved through numerous experiments proving the framework’s applicability for early diagnosis and, therefore, better management of diabetes. Further, the framework reduced the False Positive Rate and False Negative Rate further by 12%, and 8% respectively than the other related work. These findings suggest that the proposed model is not only accurate but also time and cost efficient for the large data that always typical of real-world healthcare services. There thus is a literature gap that this research seeks to fill in the quest to enhance the prediction of adverse events in healthcare delivery through advancing the methods used in predictive analytics for early IT intervention.