Both individuals and the wider society experience undue effects from extensive psychological disorder which is known as depression. The most essential factor for better patient outcomes includes timely diagnosis combined with immediate treatment. The research analyzes latest depression detection methods using natural language processing through deep learning models which process a variety of inputs from social media postings to patient file systems and multi-domain information repositories. The review establishes two significant trends showing that social media data controls more than 55% of research while transformer models, specifically BERT delivers 93.8% mean accuracy. This paper tracks linguistic and behavioral determining factors between different data resources to better understand their capacities for detecting depression. The proposed adaptive temporal-contextual transformer model (ATCTM) uses event-triggered updates together with personalized dynamic embeddings to detect depression while adapting to changes that occur in the condition’s manifestation throughout time. This paper evaluates AI-based detection method capabilities through the analysis of both strengths and weaknesses that involve model interpretability problems alongside data bias and ethical factors. Future research needs to focus on developing continuous monitoring technologies alongside multimodal integration systems and the review demonstrates how AI and NLP together can revolutionize depression detection capabilities.