Road accidents are a major cause of injuries, fatalities, and traffic disruptions worldwide. Timely detection of vehicle accidents is critical for providing quick emergency response and reducing the impact of such incidents. This project presents an AI-powered real-time Vehicle Accident Detection system developed using Python, OpenCV, and Deep Learning techniques. The system analyzes live video streams or recorded footage to automatically detect and classify vehicle accidents.The proposed approach uses computer vision methods through OpenCV to process video frames and extract meaningful visual information. A Convolutional Neural Network (CNN) is trained on image and video datasets containing accident and non-accident scenarios. The CNN model learns spatial features such as vehicle movement, collision patterns, and sudden changes in motion to accurately identify accident events. Once an accident is detected, the system can generate automatic alerts to notify concerned authorities or emergency services.This project demonstrates an end-to-end implementation, starting from dataset preparation and model training to real-time deployment. The system improves accident detection accuracy compared to traditional methods and reduces dependency on manual monitoring. Overall, this AI-based accident detection system highlights the effective use of deep learning and computer vision technologies to enhance road safety and support faster emergency response mechanisms