As mental health awareness increases, there has been growing interest in developing machine learning models to assist with early detection of depression. Building on prior studies that have explored this task, this study aims to replicate and extend previous findings using a different dataset and models. Approximately 10,000 comments were collected from each of the subreddits r/depression and r/AskReddit, resulting in a dataset of roughly 20,000 Reddit comments. The task is a binary classification problem to detect depressive language and to compare the performance of classical machine learning models with modern deep learning models. The classical models—Logistic Regression, k-Nearest Neighbors, and Multinomial Naive Bayes, were evaluated alongside a feedforward neural network and a fine-tuned BERT model. Results show that BERT achieved the highest performance at approximately 85.2% accuracy and 86.2% recall for the depressed class. However, Multinomial Naive Bayes had the second best performance with roughly 79.8% accuracy and 88.7% recall for the depressed class despite its simplicity. These findings suggest that in some applications, it may be advantageous to accept a modest reduction in predictive performance in exchange for substantially lower computational costs and model complexity.