Artificial Intelligence Driven Behavioral Analytics for Multimodal Mental Health Monitoring and Adaptive Recommendation

Mental health disorders are increasingly prevalent worldwide, yet current assessment and intervention strategies remain episodic, subjective, and insufficiently adaptive to dynamic behavioral changes. While recent advances in artificial intelligence enable continuous monitoring through heterogeneous data streams, most existing approaches rely on unimodal modeling or static learning frameworks that fail to capture longitudinal physiological dependencies and personalized behavioral responses. This paper presents an artificial intelligence–driven multimodal behavioral analytics framework for continuous mental health monitoring and adaptive recommendation. The proposed system integrates physiological signals, behavioral indicators, and contextual features into a unified representation. Temporal physiological dynamics are modeled using a Long Short-Term Memory (LSTM) network, while behavioral and contextual attributes are encoded via a Multilayer Perceptron (MLP)-based autoencoder to obtain compact latent embeddings. These representations are fused into a hybrid state space that guides a Deep Q-Network (DQN) reinforcement learning agent for personalized intervention generation. Experimental evaluation on a comprehensive multimodal dataset demonstrates stable sequence modeling, high behavioral reconstruction fidelity, and consistent reinforcement learning convergence. The proposed framework achieves improved adaptive decision performance compared to static baseline models, validating the effectiveness of reinforcement-driven personalization. The system enables early risk identification, continuous monitoring, and intelligent decision support, offering practical relevance for scalable real-world digital mental health applications. Interpretability, ethical considerations, and deployment implications are also discussed.