Diabetes prediction is a crucial focus in medical research due to the high prevalence and severe health implications of the disease. Early and accurate prediction can significantly enhance patient outcomes through timely interventions. This study examines diabetes prediction among women of Pima Indian heritage, all aged at least 21 years. Utilizing a comprehensive dataset of 2000 patient records, the study proposes an ensemble model that maximizes predictive accuracy. The approach combines the strengths of both machine learning models to build a strong predictive model. This synergistic methodology effectively balances the trade-off between model complexity and predictive power. To address potential over-fitting, an analysis model is deployed, which proved effective in ensuring the model's generalizability to new, unseen data. It demonstrates the efficacy of using ensemble models for medical predictions and highlights the potential for broader application across different populations and chronic diseases. The proposed model achieved a high accuracy of 98.37%, demonstrating the effectiveness of ensemble methods for medical prediction. This approach holds potential for broader application across diverse populations and chronic diseases.