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Diabetes is a global health issue that leads to severe complications if not detected early. In this study, we analyze a large diabetes prediction dataset using three classical machine learning (ML) models—Decision Tree, Random Forest, and Support Vector Machine (SVM)—alongside a deep learning (DL) model implemented with a neural network. We preprocess the data, perform model training and evaluation, and visualize model performance. The results indicate that the deep learning model achieves the highest accuracy, though Random Forest provides strong performance with less computational overhead. This research demonstrates the potential of artificial intelligence for early disease prediction and supports its integration into medical decision-support systems.