Diabetes mellitus is a global health concern with increasing prevalence, making early risk prediction crucial for effective prevention and management. This study conducts a comprehensive comparative analysis of Support Vector Machines (SVMs) for diabetes risk prediction, considering various SVM kernel functions and feature selection techniques. Leveraging a diverse dataset encompassing clinical, genetic, and lifestyle factors, we aim to identify the most proficient SVM-based model for accurate diabetes risk assessment. Our findings indicate that SVMs, particularly those utilizing the Radial Basis Function (RBF) kernel and optimized feature selection, hold significant promise in enhancing diabetes risk prediction. This research contributes to the advancement of precise and practical diabetes risk assessment models.