A chronic disorder that affects blood glucose levels, diabetes mellitus is caused by either cellular resistance or inadequate insulin and can lead to problems such as renal and heart disease. Support Vector Machines (SVM) are investigated in this work for the prediction of diabetes, with an emphasis on clinical application, model validation, and performance measures. SVM exhibits good prediction capabilities when used with feature selection methods like PCA and kernel functions like RBF. The performance of hybrid models, such GA-SVM, is further improved. SVM's ability to classify high-dimensional data offers promise for early diabetes detection; nonetheless, regularization and data imbalance management need to be improved.