Paper Title
Iot and Machine Learning Based Fastest Route Suggestions During Accident Detection & Alerting System

Abstract
As economies around the world strengthen and more individuals become financially capable, more people now own their own cars. Even with advancements, road infrastructure is unable to support the expanding population. As a result, there are more road accidents. As IoT technologies have advanced, it is now necessary to have a system that can instantaneously notify the responsible authorities of all relevant information regarding the frequency of a traffic accident. Tens of thousands of lives could be saved if this study's recommended use of IoT in this situation is implemented. We have coupled machine learning and image processing with IoT to accurately identify a traffic accident. To ascertain if an accident has occurred or not, a microprocessor takes data from sensors such an accelerometer, gyroscope, camera, etc. and compares it to a machine learning model. If so, the gadget uses the internet to send the pertinent metrics to the server. Various sensors, like GPS, GSM, and IoT, are employed to detect collisions and pinpoint the exact location of the vehicle. The mishap vehicle's position can be ascertained by using the Google Maps API to obtain the vehicle's Geo-Coordinates from GPS. The ambulance will be able to get there faster and save more lives as a result. The alcohol monitor can also be used to control the engine's ignition to prevent accidents. Keywords - GPS, GSM, Iot, Accident Detection, Roadmap, Alert System, Accelerometer, Android Application