One of the most common chronic illnesses, diabetes affects millions of individuals annually and has a major negative economic impact. The two main characteristics of diabetes are either insufficient insulin production by the body or inefficient use of the insulin that is produced. People become less able to control blood glucose levels, which can lower their quality of life and shorten their life expectancy. During digestion, various foods are converted into sugars, which are then released into the bloodstream. The pancreas releases insulin in response to this. Insulin makes it possible for the body’s cells to use the blood sugars as fuel. Chronically elevated blood sugar levels in diabetics are linked to complications such as kidney disease, lower limb amputation, heart disease, and vision loss. Diabetes cannot be cured, but many patients can lessen its effects by adopting healthy eating habits, exercising, decreasing weight, and getting medical care. Predictive models for diabetes risk are crucial tools for the general population and public health officials since early diagnosis can result in lifestyle modifications and more successful treatment. Gradient Boosting, AdaBoost, XGBoost, LightGBM, Bagging Classifier, Extra Trees, Neural Network, Stacking Classifier, Random Forest, KNN, Decision Tree, Random Forest, and Logistic Regression were all tested in this study. Because of its highest accuracy, F1-Score, and ROC-AUC, gradient boosting is the best model. It also performs well in precision and recall, and it has good class differentiation.