This article presents a comparative analysis of machine learning algorithms for diabetic disease identification using the PIMA Indian Diabetes Dataset. Algorithms evaluated include SVM, DT, LGR, GDBM, KNN, XGBM, and RF. Performance metrics such as accuracy, precision, recall, F1-score, ROC, and K-fold validation were used. Six test cases were performed, and the random forest algorithm achieved the best performance in the 70%-30% split. The study demonstrates the crucial role of MLAs in early diabetic prediction.