This study presents an optimized approach for detecting mental disorders by integrating support vector machines (SVM) enhanced through Minimum Bayes Error Rate (MBER) optimization. The proposed framework uses MBER Optimization and refines classification boundaries through SVMs improve decision-making. Unlike conventional deep learning approaches that rely solely on CNN based end-to-end learning, our method uses SVM for classification that minimizes errors, enhancing model robustness and generalization. The experimental evaluation on EEG-based datasets assesses the effectiveness of the hybrid approach in terms of accuracy, computational efficiency, and scalability. The results provide insights into the potential of MBER-optimized SVM models for real-world applications in mental health diagnostics.