This literature review examines studies related to real-time smart road accident detection systems using IoT, sensors, communication technologies, and intelligent emergency response methods. The reviewed studies show that accident detection systems commonly use accelerometers, gyroscopes, GPS, GSM/GPRS, microcontrollers, cloud platforms, dashboards, and real-time alerting mechanisms to reduce delays in emergency notification. Some studies focus on low-cost vehicle-mounted systems, while others explore broader emergency response platforms, V2V/V2I communication, smart traffic lights, driver monitoring, and AI-based accident or incident detection. The reviewed studies also show that machine learning, deep learning, computer vision, YOLO-based models, and multi-agent systems can support accident recognition and traffic monitoring. However, several limitations remain, including limited real-world testing, false alarms, threshold sensitivity, weak emergency coordination, connectivity problems, limited sensor fusion, and security or privacy concerns. Overall, the reviewed studies support the need for a practical hybrid system that combines IoT-based sensing, location tracking, reliable communication, emergency alerting, and, where suitable, AI-based validation for faster and more reliable road accident response.