This research investigates the predictive modeling of depression through language patterns in online discourse, specifically analyzing user-generated content from Reddit’s “depression” subreddit. Leveraging a dataset acquired via the Pushshift API, spanning January 1, 2015, to January 2, 2023, we applied advanced natural language processing (NLP) techniques—including Tokenization, StopWords removal, Lemmatization, and Word2Vec vectorization—to preprocess the text data. The study’s core objective was to determine the efficacy of text-based features in predicting depression-related posts. To this end, a variety of machine learning models were trained and evaluated, including Logistic Regression, Random Forest, LSTM, Bi-LSTM, and Bi-LSTM-RNN. Our findings are quantified through precision, recall, and F1 scores, with a particular emphasis on model performance in accurately classifying depressive content. The comparative analysis reveals significant insights into the language of depression, providing a framework for future research and potential real-world applications in mental health monitoring and support systems.