Suicidal thoughts and actions are often associated with depression, a common mental illness that is a major cause of impairment worldwide. People can now freely communicate their opinions and emotions thanks to social media sites like Twitter, which have transformed communication. These sites can be quite helpful in keeping track of health-related issues and developments. To keep people from falling victim to this depressing mental illness, this study presents a fresh technique and experimental approach to using social networking sites for more effective depression analysis. In order to discern between depressive and non-depressive content, we pre-processed, balanced, and employed a variety of machine learning approaches on about 10,000 tweets. By using both human coding and machine categorization, the study was able to demonstrate the efficacy of this strategy and achieve equivalent levels of accuracy. Using machine learning, data mining, and feature extraction methods, we illustrated how easily emotional states might be recognized. Logistic regression (LG) was the most accurate of the four prediction models assessed in identifying if a person is depressed. This creative approach demonstrates the potential of social media for early detection and monitoring of mental health issues.