This research investigates machine learning models for predicting mental health consequences using survey data. The study uses a two-phase approach: employing Tensor Flow for initial deep neural network (DNN) model building, and then using random forest (RF), naive Bayes classifier, and decision tree approaches for comparative analysis. The DNN model demonstrates strong performance, achieving high accuracy in mental health prediction. Metrics like testing time, precision, mean absolute error, and accuracy may all be compared to provide insight into the advantages and disadvantages of each model. While the DNN model excels in accuracy and precision, other models offer trade-offs in computational efficiency. The results help clarify the function of machine learning for mental wellness evaluation and intervention, offering direction for further study and real-world implementations. This research enhances the discourse on predictive modeling for mental health outcomes, facilitating advancements in leveraging machine learning for improving mental health assessment and intervention strategies.