This research explores the complexities of predicting psychology by leveraging powerful coding to generate ideas and suggestions. Carefully navigates datasets from reputable sources using rigorous data science techniques to resolve important missing and categorical features. Experiments are performed using standard model selection methods with a variety of algorithms, including neural networks in TensorFlow. In general evaluation, the AdaBoost Classifier performs better compared to other models with accuracy of 81.74. This model has been subjected to robust and precise analysis to strengthen its reliability. Our proposed model shows theoretical framework for practical application and provides a valuable resource for those working in the field of machine learning for mental health prediction.