 Early detection of diabetes is essential to prevent serious complications in patients. This study evaluated five machine learning models, Caneer's neighbor, KNN, Bernoulli-Nyeve-Base, BNB, Decision Tree, DT, Logistic Regression, LR, and Support Vector Machine SVM, to determine which model would provide the highest accuracy in detecting diabetic patients. The KNN and BNB models were found to have the highest accuracy rates at 79.6% and 77.2%, respectively. These findings suggest that ML models could be used to accurately identify diabetic patients and help reduce the risk of developing serious complications. This article was authored by Orlando Iperigirvinueva, Carina Espinola-Linares, Rosalind Ornello-Floris, Costanieta, and others.