Diabetes is a health condition that is caused mainly by high sugar levels in the blood. It is often downplayed and should be as well. If not controlled properly, diabetes has the potential to become severe and may affect one’s eyes, kidneys, heart, and other major parts of the body. In this paper, ensemble methods are applied alongside various machine learning classification approaches, including but not limited to random forest, gradient boosting, support vector machines, decision trees, KNN, and logistic regression, to predict the likelihood of developing diabetes. Each approach provides a distinct level of prediction accuracy. Consequently, this paper aims to identify the diabetes prediction model with the highest accuracy. The results from the different techniques indicate that the decision tree achieves the highest accuracy among all the data-driven methods tested.