Diabetes is a chronic condition and global health challenge, while prediabetes represents a critical stage for preventing disease progression. As prediabetes affects millions worldwide, accurate and early risk prediction tools are needed beyond blood sugar levels or HbA1c. Identifying at-risk individuals can support timely lifestyle and clinical interventions using individualized, noninvasive clinical features. This study proposes a novel multiclass Siamese Neural Network (SNN) approach for prediabetes prediction. By learning complex feature relationships from multidimensional healthcare data, the SNN model achieved 78.2\% accuracy, 88.9\% specificity, and an AUC of 0.91 using the DiabHealth dataset. These results demonstrate its ability to improve diagnostic precision despite imbalanced data and limited labeled samples. The findings highlight the potential of SNN-based predictive models for early detection, including previously unrecognized prediabetes risk features. Variable importance identified autonomic nervous system testing, oxidative stress, and diet as leading predictors ahead of fasting blood glucose. Integrating AI-driven diagnostics can improve decision-making, reduce long-term complications, and strengthen diabetes prevention efforts.