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The integration of artificial intelligence (AI) into the field of mental health diagnosis has garnered increasing scholarly and clinical attention, particularly in relation to the early detection and classification of depression. This study offers a comprehensive review of the current landscape of AI-driven approaches for depression diagnosis, examining the methodologies, data modalities, and performance metrics employed across recent empirical investigations. Emphasizing machine learning and deep learning techniques, the study critically evaluates the utility of linguistic, behavioral, and physiological data sourced from social media, clinical interviews, speech recordings, and wearable devices. The findings suggest that AI systems, particularly those incorporating multimodal data fusion and advanced neural network architectures, demonstrate promising diagnostic accuracy and the potential to augment traditional psychiatric assessments. However, the study also identifies significant methodological, ethical, and practical challenges, including issues of dataset bias, algorithmic transparency, and clinical applicability. In response, the paper outlines key future directions aimed at improving model generalizability, enhancing interpretability, and fostering ethically responsible deployment in real-world settings. This review not only elucidates the transformative capacity of AI in mental health diagnostics but also provides a roadmap for advancing the development of robust, transparent, and clinically integrated AI systems for the detection of depression.