Mental health issues, including depression, anxiety, and PTSD, are on the rise, especially in regions like Bangladesh where access to mental health resources is scarce. Social media platforms serve as a valuable source of user-generated content, offering an opportunity for early detection of mental health concerns through shared experiences and emotions. This paper introduces “MindScope,” an AI-based system designed to detect mental health issues from Bangla social media texts using machine learning and deep learning models. The dataset, gathered from platforms like Facebook, Twitter, and Reddit, underwent preprocessing including tokenization, stop word removal, and TF-IDF vectorization. Multiple machine learning models, such as Random Forest and Logistic Regression, were tested alongside deep learning architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). The Random Forest model achieved an accuracy of 89%, while the LSTM model demonstrated a superior performance with an accuracy of 89% in identifying mental health-related content. These findings highlight the potential of AI-driven approaches in supporting mental health monitoring and early intervention for Bangla-speaking communities.