Diabetes is chronic disorder it affects millions of people throughout world. It can be cured diagnosis, with proper treatment. With aid of various techniques developed and created using machine learning algorithms can identify, predict type of diabetes. Machine learning is becoming more and more popular today. Therefore, technique has been used in various medical scenarios. This work, we used Machine Learning Approach’s (MLA) and different measurement metrics such as precision, recall, Fi Score. This work, numerous supervised classifiers using machine learning is compared based on efficiency of multiple factors for early diagnosis of diabetes. Six MLA have successfully used in experiment research. With an accuracy rating of 98% Random Forest Classification (RFC) outperforms other classifiers for predicting early diabetes mellitus. In this study a framework for early diabetes prediction is developed. In addition, the dataset classification skew has been eliminated using the ten-fold cross-validation procedure.