Mental illnesses have become a serious health issue of concern, and new mechanisms of surveillance and early detection are needed. All of that is presented in this study, which proposes a sophisticated system of mental health monitoring through the combination of Natural Language Processing (NLP) and Recurrent Neural Networks (RNNs). Based on textual information of various sources, such as social media, personal diary, or online discussion groups, the proposed system is able to detect early signs of mental health problems, including depression, anxiety, stress, etc. The system uses high accuracy in identifying mental health patterns through two advanced techniques of NLP feature extraction and use of RNNs in the modeling of sequences. Django platform forms a reliable and scalable web-Based platform to real-time monitoring whereby these characteristics include user authentication, interactivity through visualization, and confidentiality of data. The findings show a marked increase in sensitivity and specificity than in conventional method making the system ideal in enhancing early detection and personalized intervention. The present piece of work can lead to betterment of technologies in mental health, proposing practical information on how individual well-being and overall quality of life can be remodeled.