This paper explores a novel ensemble machine learning-based approach for indicating mental health in terms of depression and depression-related states among users by considering the Reddit dataset. Reddit being one of the bigger social media platforms, provides a rich source of data along with anonymity where individuals can openly share their thoughts without the fear of judgment. Utilizing natural language processing (NLP) methods and ensemble machine learning algorithms, our models capture and analyze patterns in the user’s language that signify mental health indicators, and we compare the different boosting ensemble machine models such as XGBoost, AdaBoost, CatBoost, etc., to assess the accuracy of prediction of mental health. The proposed method involves the following steps (i) data collection, (ii) data pre-processing, (iii) Implementing boosting algorithms and provides the gradation of data sources and techniques that have been used for mental health care and monitoring. Through our method, we demonstrate measurable accuracy in identifying depressive and depression-related content yielding promising quantitative results that support its potential application for mental health monitoring and intervention. Unigrams when combined with all the ensemble learning methods such as XGBoost, CatBoost, AdaBoost, and MART gave us more than 0.93 accuracy always, Bigrams when combined with all the ensemble methods gave us at least 0.84 accuracy in all cases and similarly Latent Dirichlet Allocation (LDA) yielded an accuracy of greater than equal to 0.90 in all cases.