Diabetes is a prevalent chronic disease with significant health implications worldwide. Early detection plays a crucial role in effective management and prevention of complications. This research presents a comprehensive study on the application of machine learning techniques for the early detection of diabetes. The study compares multiple machine learning algorithms, explores data preprocessing techniques, and proposes an ensemble model to enhance accuracy and reliability. This research paper contributes to the field of early diabetes detection by developing an accurate and reliable machine learning model. The proposed ensemble model, combining SVM and Random Forest Classifier, surpasses individual algorithms in terms of accuracy. The research paper provides valuable insights for identifying individuals at risk of diabetes, facilitating timely interventions and improved healthcare outcomes.