Diabetes is a condition characterized by elevated blood glucose levels, which should not be overlooked as it can lead to severe complications and organ damage if left untreated. Effective management of diabetes relies on early prediction. Traditional diagnostic methods can be both financially and temporally burdensome for patients and may also be prone to inaccuracies. To address these challenges, modern approaches are proposed. In this project, we aim to achieve early prediction of diabetes in humans by employing various machine learning techniques. Two classification models, XGBoost and LightGBM, were developed based on the Pima Indians Diabetes Database, with hyperparameter tuning to optimize their performance. Both models were rigorously evaluated using various metrics, including Accuracy, Precision, Recall, F1-score, and False Negative Rate. Ultimately, this study achieved an accuracy of 83% with the LightGBM algorithm. After comparing the models, we observed that both demonstrated considerable accuracy in distinguishing diabetic patients, but the LightGBM model exhibited superior performance compared to XGBoost. Based on these findings, the LightGBM model was selected for the development of a web application prototype using the Django framework, which was subsequently deployed for pilot testing, yielding satisfactory results. Our future endeavors will focus on increasing the model's accuracy and completing the necessary steps for its real-world implementation.