One of the chronic diseases that affect many people worldwide is diabetic mellitus. If the disease is predicted at an early stage, the risk and severity can both be significantly decreased. In this research, we need to predict the type 2 diabetic patients at an early stage to reduce the cost of treatment for countries because this is a long time disease we use many machine learning algorithms to find the accuracy for these diseases applied to BRFSS datasets for two years 2014 and 2015 with a different selection of features to predict the disease as decision tree, logistic regression, ADA Boost Classifier, extreme gradient boosting, Linear Discriminant Analysis, Light Gradient Boosting Machine, and catboost classifiers. While applying our experiments with the 2014 BRFSS data sets Neural network has the highest accuracy with 82%and with the 2015 BRFSS datasets the best accuracy model was 86% for CatBoost Classifier and Extreme Gradient Boosting where the lowest model was Linear Discriminant Analysis. Also, in our research we compare our results with others using the same datasets with different features selection and get high accuracy.