Early Detection of Mental Health Disorders Using AI: A Comprehensive Review

Mental health disorders affect over 970 million people globally, posing a major public health challenge. Traditional diagnostic methods, based on interviews and self-reports, often lead to delayed intervention. This review explores how artificial intelligence (AI) is being used for the early detection of mental health conditions, focusing on model accuracy, methods, and applications across various data types. We examine 12 key studies from 2019 to 2025 covering voice analysis, multimodal deep learning, social media monitoring, wearable sensors, and electronic health records. Notably, AI model accuracy has improved from 82.4% to 99.06%, with multimodal systems like NeuroVibeNet leading in performance. AI systems can detect mental health issues an average of 7.2 days earlier than clinical methods. Voice-based tools show 71.3%–92.0% accuracy, social media models reach 89.3%, and wearable devices detect depression with 90% accuracy. However, challenges remain in privacy, fairness, interpretability, and clinical adoption. Future research should focus on explainable AI, privacy-preserving methods like federated learning, and real-world validation. AI’s integration with diverse data sources offers promising potential to enhance mental health care through earlier, more accurate detection.