Diabetes, characterized by elevated glucose levels in the human body, poses significant health risks if left untreated, including heart issues, kidney dysfunction, hypertension, eye damage, and harm to other organs. Early prediction and intervention are critical for effective diabetes management. In pursuit of this objective, this project employs various machine learning techniques to enhance the accuracy of diabetes prediction. Machine learning, a potent tool for predictive analytics, leverages patient datasets to construct models. In this study, we apply classification and ensemble techniques within the machine learning framework to predict diabetes. The selected algorithms encompass Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). Each machine learning model exhibits distinct accuracy levels when compared to its counterparts. The central aim of this project is to identify the most accurate model, highlighting its proficiency in predicting diabetes effectively. Our findings demonstrate that the Support Vector Machine (SVM) model outperforms other machine learning techniques, showcasing its potential for early diabetes prediction. This research seeks to contribute to improved healthcare outcomes by enabling early intervention and enhanced disease management.