Accidents are occurring increasingly frequently and at an alarming rate as a result of the quickly increasing number of vehicles on the road. certain the increased number of traffic incidents and fatalities today, the ability to anticipate the number of accidents over a certain time period is essential for the transportation department to make wise decisions. In this case, it will be advantageous to examine the accident frequency so that we may make use of this knowledge to build tactics to minimise them. Even if the majority of accidents have ambiguous characteristics, a certain amount of regularity can be seen when incidences are observed in one place over time. This regularity can be used to make precise predictions about the probability of accidents happening in a specific region and to develop accident prediction models. In this project, we looked into how road conditions, environmental factors, and traffic accidents are related to one another. We have created a data mining-based accident prediction model using GridsearchCV and the Random Forest Classifier. A road traffic accident dataset that was publicly available online was used for this inquiry. The study's findings can be used to improve the construction of roads and automobiles by a variety of parties, including but not limited to the government's public works agency, contractors, and other automobile businesses.