Mental health has turned out to be a global issue with the presence of digital communication, and there is an increasing scope for textual data to be mined on one’s emotional and physiological health. This research examines the combination of sentiment analysis and a predictive model using NLP to detect and analyze mental health conditions from text data. The primary objective of this research is to evaluate predictive models that would be capable of detecting mental health conditions from sentiment analysis of text data. Involving in this task in the testing and comparing different types of NLP architectures including Transformer models and traditional ML models to see which of these methods is better in identifying signs of mental health conditions. This study uses a rich dataset that is collected from multiple sources like social media text, personal diaries, etc. This study trained multiple ML algorithms such as the Transformer model (RoBerTa, BERT, and DeBERTa) and Non-Transformer model (Naive Bayes, Random Forest, Logistic Regression) to determine the mental health classification of the text data. This work evaluated multiple ML algorithms for detecting mental health categories, highlighting the one best model that consistently best performs with high \(F_1\) -scores among the rest. The model accuracy was high, and errors were few. As a whole, this study makes an important contribution to mental health and offers the basis for developing tools that could help both professionals and individuals.