The majority of fatalities and serious injuries occur as a result of incidents involving motor vehicles. If the traffic  management system is going to do its job of reducing the frequency and severity of traffic accidents, it needs a model for doing so. In this paper, we combine the results of three machine learning algorithms—logistic regression, decision tree, and random forest classifier—to build a predictive model. In order to forecast the severity of accidents in different regions, we used ML algorithms on a dataset of accidents from the United States. In addition, we examine vast quantities of traffic data, extracting  helpful accident patterns in order to pinpoint the factors that have a direct bearing on road accidents and make actionable suggestions for improvement. When compared to two other ML algorithms, random forest performed best on accuracy. The severity rating in this paper is not meant to reflect the severity of injuries sustained, but rather how the accident affects traffic flow. Accident severity, decision trees, random forests, and logistic regression are all terms that are often used to describe this area of study