Mental health care in the modern world constantly changes; therefore, treatments need constant development to achieve better results, and diagnoses must be determined more accurately. In our study, we show in great detail how the latest research points to smart ways of using machine learning and deep learning algorithms to change the game in mental health services. We focus on three special algorithms: Random Forest, Support Vector Machine, and K-Means Clustering to see if we could predict how people are feeling in terms of anxiety and depression. We used two sets of data for this: one set from the surveys, which ask about anxiety and depression, and another set that anyone can find on Kaggle—one of the more popular data science websites. By tuning each of the algorithms just so, preparing our data just right, and putting them together in one sturdy model, we found that our strategy made considerably more accurate predictions than retro methods. It is not just about numbers and data; it is also about finding a way of knitting different tools together to understand mental health. Adding this into the mix, our work contributes to a larger conversation of how technology can contribute to the diagnosis of mental health problems and opens up possibilities for the development of treatments that fully meet the needs of an individual.