A Multimodal AI Framework for Early Detection of Mental Health Disorders using Emotion Analysis: A Comprehensive Review

Mental health conditions like stress, anxiety, and depression have become major global concerns, affecting people of all ages and backgrounds. Early detection remains challenging because mental health assessment often relies on subjective evaluation, social stigma, and the limited availability of trained professionals, particularly in resource-constrained environments. With the growth of artificial intelligence and affective computing, researchers are exploring automated ways to understand emotional and behavioral signals for mental health evaluation. Emotions expressed through written text, facial expressions, and speech patterns can reveal meaningful indicators of a person's psychological state. However, relying on only one type of data may lead to incomplete or inconsistent results, since emotional expression differs across individuals and situations. This review examines AI-based multimodal methods that analyze text, facial cues, and speech for mental health understanding. It organizes existing research by feature extraction methods, learning models, emotion-to-mental-health relationships, and fusion strategies used to integrate different modalities. It also reviews commonly used datasets, evaluation practices, and performance patterns. The paper further highlights ongoing challenges such as dataset bias, ambiguity in emotional interpretation, limited interpretability, privacy concerns, and insufficient clinical validation. Finally, it outlines future directions, including explainable AI, ethical implementation, real-world deployment, and stronger collaboration with healthcare systems to build trustworthy mental health assessment tools.