The analysis of depression through social media platforms has become an important and promising research area, enabling early detection of mental health issues by identifying linguistic patterns in textual data. This study reviews existing research in depression detection and presents a concise overview of machine learning techniques applied to social media text, along with a comparative analysis of their performance. Based on this review, a novel approach is proposed for depression detection using an improved recurrent neural network (RNN) architecture combined with GloVe embeddings. The research focuses on developing an RNN-based model capable of classifying depressive expressions in user-generated content. By incorporating GloVe (Global Vectors for Word Representation) embeddings, the model effectively captures semantic relationships and subtle linguistic features present in social media text. This integration enhances the model’s ability to identify nuanced emotional cues and complex language patterns associated with depression. The model is trained using a Twitter dataset and further evaluated on an additional dataset to ensure its robustness and cross-platform applicability. Experimental results demonstrate that the proposed approach achieves a high test accuracy of 98.45%, indicating its efficiency and reliability as an automated tool for depression detection in real-world social media environments