The proposed project is a smart and automated accident detection and emergency response system designed to improve road safety through real-time monitoring and fast alert generation. The system continuously monitors live road camera feeds and uses the YOLOv8 deep learning model to detect accident and fire incidents automatically. Unlike traditional monitoring systems that depend on human observation, this project provides an intelligent solution that can recognize critical events without manual intervention. The web-based dashboard allows the admin to log in, add station details, manage connected cameras, and monitor multiple stations from a centralized interface. The backend of the system is developed using Python and Flask, while the frontend is created using HTML, CSS, and JavaScript to provide a simple and user-friendly experience. Firebase Realtime Database is integrated to store station data, event records, and alert information with instant synchronization. When the system detects an accident or fire, it saves the event in Firebase, identifies the linked station, and retrieves the responsible person’s contact details. After that, the alert information is sent to the hardware communication module. The hardware side of the project uses ESP32 and SIM800L GSM module to generate automatic emergency alerts. Through this module, the system can make phone calls and send SMS notifications directly to the concerned authority. In addition, the system also sends email notifications with accident details and related evidence, which helps emergency teams understand the situation quickly. A nearby emergency response feature is also included so that hospitals, police stations, and fire brigades can be informed in less time. The model is trained on a custom dataset using Google Colab, where accident and fire-related images are prepared in YOLO format for real-time object detection. The overall system combines AI-based detection, cloud database integration, web dashboard monitoring, and hardware-based communication into one complete platform. This makes the project a practical and modern solution for intelligent traffic surveillance, automated emergency response, and smart city road safety systems.