Ai-Driven Behavioral Data Analytics for Mental Health Predicting User Trends and Providing Actionable Insights

Mental health disorders are rising globally, yet traditional assessments remain episodic, subjective, and limited in scalability. This paper presents a hybrid multimodal artificial intelligence framework that integrates physiological signals, behavioral analytics, and medical history to enable continuous mental-health monitoring and adaptive intervention. The proposed architecture employs a Long Short-Term Memory (LSTM) network for physiological trend modeling, a Multilayer Perceptron (MLP) autoencoder for behavioral feature extraction, and a Deep Q-Learning (DQN) agent to generate personalized, real-time therapy recommendations. Multimodal feature fusion enhances overall prediction accuracy and supports adaptive learning through user feedback. Experimental results demonstrate that the LSTM achieves a mean squared error of 0.032 and coefficient of determination of 0.76, while the DQN recommendation model attains 72 percent accuracy with stable reward convergence. The framework ensures data privacy and ethical integrity under General Data Protection Regulation (GDPR) compliance and is designed for future integration with federated learning. This study demonstrates that combining deep learning with reinforcement learning significantly improves personalized mental health prediction and adaptive care delivery compared with single-modality approaches.