The growing number of people suffering from mental health disorders, especially depression, highlights the need for scalable, real-time monitoring tools. With the rise in online conversation, particularly on standalone sites like Reddit, Natural Language Processing (NLP) has become a strong tool in terms of identifying depression in an early stage. This work examines the performance of the three top transformer-based models-MentalBERT, FacebookAI/roberta-base, and DistilBERT-base-uncased-emotion-to distinguish between depressive and non-depressive text content. The models were fine-tuned on dataset and tested on unseen Reddit content from the r/History subreddit, with performance evaluated through key parameters such as accuracy, precision, recall, F1-score, and confusion matrix measures (TP, TN, FP, FN).Among the models compared, MentalBERT was found to perform the best. Analysis of the confusion matrix also manifested MentalBERT’s strength in reducing false negatives and false positives and thus emerged as the best practical-deployment model. The findings demonstrate the strength and weakness of present transformer models in grasping complicated patterns of language related to mental health. By integrating computation with psychological testing, this work adds to the developing area of AI-aided mental health monitoring. The work not only identifies the best-performing model but also lays a foundation on which to develop ethically grounded, precise, and scalable NLP-based assistive tools to support mental well-being in real-world online spaces.