This study evaluates machine learning algorithms—Neural Network, SVM, Logistic Regression, and Naive Bayes—for classifying mental health conditions, including major depressive disorder, bipolar disorders, PTSD, and controls. Neural Networks achieved the highest accuracy (89.5%) with balanced precision (0.88) and recall (0.89), demonstrating ML's potential to improve diagnosis and inform individualized treatment plans for mental health assessment.