Deep Learning in Depression Detection: A Comprehensive Survey and Critical Analysis

Depression is a significant global health challenge, affecting millions worldwide and contributing to severe disability. Recent advances in deep learning have enabled more effective detection of depressive symptoms across multiple modalities, including text, audio-visual, neurophysiological signals, and multimodal fusion. This survey systematically reviews the evolution from traditional machine learning (e.g., Support Vector Machines, Random Forests) to state-of-the-art deep learning architectures(e.g., CNNs, LSTMs, Transformers, and Graph Neural Networks) and Large Language Models. We analyze benchmark datasets, model architectures, and performance metrics while identifying key challenges such as data scarcity, domain adaptation, cross-subject variability, and real-world deployment barriers. Unlike previous surveys that focus on single modalities or lack technical depth, we provide a comprehensive synthesis of deep learning methodologies, architectural insights, and ethical considerations related to privacy, bias, and clinical trust. We conclude by outlining a research agenda that integrates cutting-edge LLM innovations (including autonomous reasoning chains and multimodal contextualization), quantum-enhanced learning architectures, and LLM - powered cognitive digital twins for personalized monitoring, while calling for specialized mental health LLMs, cultural adaptation benchmarks, and HIPAA-compliant federated learning frameworks. This survey serves as both a technical reference for researchers and a roadmap for advancing AI -driven depression detection toward scalable, ethical, and clinically relevant applications.