AI-Based Approaches for Early Detection of Major Depressive Disorder: Technical Roadmap and Future Directions

Major Depressive Disorder is a highly prevalent and debilitating mental condition, frequently co-occurring with anxiety or sleep disorders and/or suicidal risk. The conventional measures of diagnosis including patient interview and PHQ scores are subjective and ineffective in the early stages of diagnosis. Recent developments in artificial intelligence have introduced the possibility of detecting subtle multimodal biomarkers in EEG, fMRI, speech, facial expressions and Social Media text. The paper provides a survey of the latest AI models-CNN, LSTM, GNN, and multimodal fusion frameworks and outlines a comparative analysis of datasets, methods, and results. Comparative results demonstrate that multimodal methods provide the best accuracy ($94-99.3 \%$), psychosocial models slightly below (99.4 %), and EEG-based models slightly under these ($93-98 \%$) with visual-only performing lowest $(\sim 81 \%)$. The study reviews strengths and limitations and outlines a roadmap for explainable, scalable, and clinically viable AI solutions