Diabetes affects millions of individuals, making it a global health issue, and is categorized as a chronic metabolic disorder. The driving purpose of this study is to develop a predictive model for diabetes based on machine learning algorithms so that early assessment of and intervention for diabetes risk can be done. Diabetes risk assessment and early detection are components of the preventive healthcare approach; the main aim is to assess and compare the performance incorporating diverse machine learning algorithms for accurate diabetes risk prediction, and each method examines the implementation and refinement of hyperparameters for best prediction performance. Such indicators include precision, accuracy, recall, F1-score, and the receiver operating characteristic (ROC) curve, which assesses the area that the curve encloses as one of the evaluation metrics. Methods such as gradient boost, XGBoost, random forest, and lightweight GBM are notably helpful since they can combine the benefits of many models to improve prediction accuracy, and the final goal is to formulate reliable and accurate predictive models. Certain evaluations are made to evaluate the effectiveness of these models, and the results of the study are necessary to create preventive medical care and adjustment of treatment of patients. This study aims to develop new health-improving methods and make a considerable impact on the prevention of diabetes, utilizing ML algorithms.