eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-10-27 18 October Spl Edition 68672 article Sushama Tanwar 1 Prashant Vats 1 Surbhi Sharma 1 Kamal Upreti 2 Department of CSE, Manipal University Jaipur, Jaipur, Rajasthan, India CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India Mental health disorders such as depression, anxiety, and PTSD present significant challenges to global healthcare systems, demanding more effective tools for early detection and diagnosis. This study proposes a multimodal AI framework that integrates behavioral data—including textual inputs, speech patterns, facial expressions, and physiological signals—using advanced deep learning techniques. By applying attention mechanisms, graph neural networks, and multi-task learning, the model captures complex patterns and temporal dynamics across modalities to enhance assessment accuracy. To address issues like data heterogeneity and feature misalignment, the framework employs robust fusion and alignment strategies. Interpretability and explainability are prioritized to build clinical trust and support therapeutic decision-making. Evaluations on benchmark datasets demonstrate notable improvements in detection performance for depression, anxiety, and PTSD. The results highlight the potential of AI-driven multimodal behavioral analytics to transform mental health diagnostics, enabling personalized and real-time interventions. Future work will explore ethical concerns, data privacy, and system scalability for broader adoption in clinical and community settings. https://biomedpharmajournal.org/vol18octoberspledition/multimodal-data-fusion-in-mental-health-integrating-behavioral-analytics-with-ai-ml-for-enhanced-detection/ Anxiety Prediction Attention Mechanisms Behavioral Analytics Clinical Integration Data Privacy in AI Depression Detection Ethical AI Systems Explainable AI Multimodal Data Fusion Mental Health Analytics Mental Disorder Detection Multi-Task Learning Personalized Interventions Physiological Signal Processing PTSD Analysis Real-Time Monitoring Speech and Text Analysis Temporal Dynamics