Mental disorders such as depression, anxiety, and suicidal ideation are rising rapidly among young people in Bangladesh, with social media providing a prominent platform for expressing oneself and emotions. Early identification of these through automatic systems can provide timely intervention and even save lives. To address this problem, we propose a hybrid CNN-BiGRU model that combines linguistic, psychological, and sentiment attributes to model semantic meaning along with long term contextual relations in Bangla text with a 94% accuracy. Our method, trained on a well-curated dataset, is more precise and has superior generalization compared to standard machine learning methods like Logistic Regression, Support Vector Machines, and Multinomial Naive Bayes. These results indicate the potential of deep learning towards effective mental health detection in low-resource languages