Road accidents frequently result in damage, injuries, or fatalities and happen unexpectedly and without warning. Through the elimination of the time lag between the accident's incidence and the initial medical emergency, this research aims to lower the fatality rates following an accident. A machine learning-based intelligent system is used in this study to detect accidents and alert the police station and hospital. A vehicle's sensors, including its speed sensor, gravitational force sensor, sound sensor, and Nodemcu-ESP8266 with Global Positioning System, are embedded utilizing an Internet of Things system (GPS).

Artificial neural networks are used to interpret and analyze the data that the sensors have collected. When an accident occurs, the sensors are able to detect it and send the data to the Nodemcu-ESP8266, which then collects the data from the sensors and sends it to the data engine. The findings of the prototype experiment used 49 input data sensors; there were 35 incidents, of which 27 were correctly recognized while 8 were not; the experiment's detection rate as a percentage was 77.14%. Accidents may be anticipated, and hospitals and police stations could be contacted. The GPS provided the GPS coordinates to pinpoint the accident's location. Using sensors and machine learning, people can be assisted in receiving first aid quickly after an accident.