Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Support Vector Machine . The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.