An excess of glucose in the bloodstream leads to the disease known as diabetes. Ignoring diabetes for an extended period of time may lead to serious complications, including but not limited to: renal disease, high blood pressure, vision loss, and heart difficulties. When caught early enough, diabetes is manageable. In order to accomplish this goal, our organisation will use a variety of machine learning approaches to improve the accuracy of early diabetes predictions in human or patient settings. By constructing models from patient data sets, machine learning approaches provide a stronger foundation for prediction. In this study, we will use the dataset to forecast the occurrence of diabetes by using clustering and classification algorithms from machine learning. These include Random Forest (RF), Gradient Boost (GB), K-Nearest Neighbour (KNN), Decision Tree (DT), and Logistic Regression (LR). When comparing several models, accuracy may vary significantly. Based on the r