This research addresses the imperative need for early diagnosis in diabetes utilizing a machine learning framework, incorporating ensemble learning and hyperparameter optimization, notably the hybrid algorithm ABC-WOA. Through an extensive literature review, existing diagnostic techniques are evaluated, identifying strengths and weaknesses. The primary objective is to develop a robust ensemble model that amalgamates diverse machine learning techniques, with a particular focus on integrating the hybrid algorithm ABC-WOA for enhanced diagnostic accuracy. The model incorporates genetic algorithms for feature selection and hyperparameter tuning, aiming for optimal performance. Using important measures that include recall, precisely, precisely, and with an F1-score, we assess the recommendation ensemble model in comparison to previous diagnostic approaches Anticipated results showcase the superior diagnostic capabilities of the ensemble learning approach enriched with the hybrid ABC-WOA algorithm. This research not only advances early diagnosis methods but also emphasizes the significance of thoughtful ensemble design, feature selection, and hyperparameter optimization in improving the accuracy of machine learning models in healthcare applications.