The risk factors of diabetes in places around the world have made the disorder quite an emergency in the global perspective, as well as an impetus for early diagnosis to avert and enhance patient care. This study concerned itself with applying machine learning (ML) methodologies for diabetes predictive care, with a greater concern on performance, clarity, and flexibility in practice. The investigation of large datasets defining combined clinical, lifestyle, and demographic variables for study and comparison of machine learning models to predict the risk of diabetes was conducted by this research work. Logistic regression, decision trees, random forests, support vector machines along with deep learning algorithm. Various techniques through feature engineering and data preprocessing such as imputation and normalization were done by the researchers to improve and optimize the model performance. Age, body mass index, and glucose levels turned out to be the primary significant determinants of diabetes. The models were evaluated on several performance metrics such as accuracy, precision, recall, and F1-score. The whole framework was built for predictive models to assess the risks of diabetes. However, neural networks and ensemble methods were definitely better at predicting. However, this would translate into a more extensive appreciation of model choices through vastly enhanced interpretability using Shapley Additive explanations (SHAP) values. The research has shown how machine learning (ML) can be applied in preventive healthcare through early diagnosis and personalized treatment regarding diabetes.