The purpose of this paper is to look into the application of machine learning techniques in the early detection of diabetes. Diabetes is the most common chronic illness that can lead to serious consequences for health, so early detection is very important for effective treatment. In this paper, nine machine learning classification models were created and tested on a preprocessed, clustered, balanced training and testing dataset. The data indicates that the best accuracy of 93.52% can be achieved by the RF algorithm. The results of this work have shown the importance of preprocessing the data and clustering in enhancing the accuracy level of predictive models and the promise of Machine Learning in early diabetes prediction. This article makes important contributions to the field of diabetes prediction and opens up possibilities for future research to increase the accuracy of ML models in early diabetes prediction.