This work aims to establish a modern standard for reducing road accident prevalence through the introduction of advanced and sophisticated technologies. Recent statistics on traffic accidents highlight the urgent need for proactive policies to mitigate risks and improve emergency response systems. Our system, designed for real-time implementation, leverages Convolutional Neural Network (CNN) models to detect accidents from traffic camera footage. The model is meticulously trained on an extensive dataset encompassing various accident scenarios, including collisions, vehicle overturns, and pedestrian incidents, ensuring high sensitivity and accuracy. This accuracy is achieved through specialized partitioning of training datasets into valid and invalid sets, resulting in a substantial training data pool. Through iterative optimization, the model enhances its ability to recognize and respond to accidents by refining its techniques and tools. This innovative approach surpasses current practices by integrating precise object detection techniques from computer vision [1] with global positioning system (GPS) capabilities. The resulting system is a significant advancement over traditional road safety management methods; the accuracy of this novel algorithm is about 96%. It not only detects accidents with high accuracy but also facilitates rapid and seamless collaboration among stakeholders, thereby reducing response times and enhancing the effectiveness of emergency plans. By fostering long-term partnerships with public agencies, transportation authorities, and technology enterprises, it is aimed to make this advanced technology a widespread solution, creating a safer and more resilient environment for residents nationwide in India.