The global health issue needs an accurate early detection model. The proposed work shows that with advanced machine learning, millions of people can be impacted by diabetes early detection. Ensemble learning focuses on XGBoost for its efficiency and scalability. It uses the Pima Indians Diabetes Dataset to assess glucose, BMI, and blood pressure methods. The stacked ensemble model, which incorporates an XGBoost base model together with a meta-model, provides improved predictions and generalization properties, thus reducing overfitting, data imbalance, and complex interactions of features in the medical domain dataset. The cross-validation is performed by Stratified K-Fold for the fair representation of cases in generating dependable predictions. The accuracy of the stacked ensemble is 75.96 models like Random Forest and AdaBoost. Moreover, integration of clinical, genetic, and lifestyle. This allows us to timely and accurately diagnosis minute markers of diabetes on early phases. The framework shows how such machine learning can transform health care by predicting risks so that prevention can be supported. It points out how ensemble learning overcomes the constraints of traditional models in diagnosis. This research also enables the optimization of ensemble models for greater medical applications, enhancing the care and outcomes for patients.