Stress, depression, and other mental health issues are increasing worldwide, affecting all ages. In response, a new program employs machine learning to find and study the origins of mental health concerns like stress, anxiety, and depression. In terms of precision and efficacy, the research incorporates a number of machine learning techniques that transcend more traditional methodologies. The data was collected from numerous social media sites using machine learning to identify early signs of serious depressive disorder. The datasets were fully analyzed to understand mental health patients' behavior. We found mental health issues' causes using facial expression, gesture, speech, and text analysis. We also used hand gestures, lip, nose, and eye movements to identify emotional states like anger, happiness, grief, and neutrality. This was done using an image and video processing emotion identification system. Our program focuses on early mental health diagnosis and treatment to assist consumers get the right help. Machine learning enhances how well mental health illnesses are diagnosed, allowing people to take the necessary actions to improve their mental health.

Supervised machine learning, medical science, Naïve biased, CNN, Image processing