Depression is a crucial problem in mental healthcare, due to its broad prevalence and grave effects on people’s lives. Individuals feel free to write about their mental state on online platforms, so a wealth of user-generated content is available on social media platforms. This research aims to identify mental health problems from social media texts. In this study, an innovative depression detection model using word embedding and deep neural network is built. The proposed study identifies people with depression with 72% accuracy and F1-score of 0.62. The experiment shows early and precise diagnosis, prompt intervention, and support for those who are at risk, all of which improve the overall well-being and mental health outcomes of people who are afflicted.