Social media is using rapidly for expressing user’s opinions and feelings. At present, ‘Depression’ is propagating in a cursory way which is the reason for the increasing rate of suicides even more. Recently, the status on social media can give the hints of user’s mental state along with the situation and activities that are happening with them. Different research works had done for perceiving the mental health of any user through social media which has impressive impact. That is done by analysing expressed opinions, images, sentiments, linguistic style and other activities. Variant studies had proposed variant methods or intelligent systems for detecting depression or state of mental health through social media posts. In this chapter, the posted tweets from users on Twitter will be considered in the sake of detecting depressions. Six machine learning approaches named Multinomial Naive Bayes, Support Vector Classifier, Decision Tree, Random Forest, K-Nearest Neighbor, Logistic Regression had used to differentiate the depressed and non-depressed users. Finally, Support Vector Classifier outperforms among all of the investigated and evaluated techniques with 79.90% accuracy, 75.73% precision, 77.53% recall and 76.61% f1-factor. This study can be a basement for the developers of intelligent systems in the area of users mental conditions detection.