Mental health disorders, including depression, anxiety, and post-traumatic stress disorder, affect over 1 billion people globally, contributing significantly to the global burden of disease and socio-economic challenges. Early identification and intervention are critical to mitigate the long-term consequences of these conditions; however, conventional screening methods often rely on self-reporting or limited clinical assessments, which can delay timely care. In recent years, emotion recognition has emerged as a promising approach to facilitate early intervention by analyzing behavioral and physiological indicators to detect subtle changes in mental states. This review examines the role of artificial intelligence (AI) techniques, including machine learning and deep learning, in processing multimodal data such as textual communication, vocal tone, facial expressions, and physiological signals for emotion detection. We summarize state-of-the-art models, including convolutional neural networks, recurrent neural networks, and transformer-based architectures, and discuss their effectiveness in real-world mental health applications. Additionally, the paper addresses key challenges, including data privacy, ethical considerations, cultural and language biases, and the interpretability of AI models. Future directions for research are highlighted, emphasizing the integration of wearable devices, multimodal fusion, and human-centered AI frameworks to enhance accessibility and reliability. By synthesizing recent advances, this review underscores the potential of AI-driven emotion recognition systems to support proactive mental health care, reduce stigma, and improve societal well-being through timely, personalized interventions.

Keywords: Emotion Recognition, Mental Health, Machine Learning, Deep Learning, Early Intervention, Human-Centered AI