Depression is a major issue for mental health worldwide and often goes unnoticed. As, it is hard to detect quickly and easily. This article introduces a new way to predict depression using machine learning and facial detection through live cameras. By analyzing facial expressions in real time, the system can detect small emotional signals that may indicate depression. It uses smart computer vision techniques with tools like OpenCV to capture important facial details, such as tiny expressions, eye movements, and how eyebrows are positioned. Several machine learning methods, such as Random Forest, Logistic Regression, AdaBoost, Naive Bayes, and SVM, are tested, and XGBoost gives the best result. This model yields an impressive accuracy of 99.21%, with a precision of 0.98%, a recall of 0. 99%, and an F1 score of 0. 98%, which shows that it is better than the others in all the ways we looked. The system works with a real-time processing setup, which means that it predicts quickly and gives easy feedback through a user-friendly interface. This work shows how machine learning and facial detection can work together for the early and easy detection of depression, providing a solution that could be used both in clinical settings and in everyday life.