In today’s interconnected world, ensuring road safety is of paramount importance, especially in challenging conditions such as low-light environments where accidents are more likely to occur. This paper introduces an innovative approach to elevate accident detection capabilities in such conditions through the use of advanced computer vision and machine learning methodologies. The proposed system integrates state-of-the-art image processing algorithms and the YOLO (You Only Look Once) algorithm, renowned for its real-time object detection capabilities. By analyzing CCTV footage in real-time, the system enhances visibility and accuracy in identifying incidents, contributing significantly to proactive incident management and emergency response times. Through rigorous testing and evaluation, the system achieved an impressive accuracy rate of 94.06%, demonstrating its effectiveness and reliability in real-world scenarios. The integration of a web application-based alert mechanism further enhances the system's capabilities by enabling rapid notification of relevant authorities and nearby individuals in case of accidents. Overall, this paper represents a significant leap forward in road safety technologies, showcasing the transformative potential of cutting-edge technologies in addressing critical challenges in urban safety and surveillance.