As reported by the World Health Organization (WHO), road accidents are the 8th leading cause of deaths worldwide. In order to reduce these fatalities, it is essential to identify the parameters that elevate the risk of road crashes. Current research utilizes various machine learning models to forecast the severity of road accidents based on factors like meteorological patterns, geographic location, and more. Accident severity prediction was achieved using five distinct models: Extra Trees Classifier, Logistic Regression, Random Forest Algorithm, XGBoost, and Decision Tree Classifier. Among these, the Extra Trees Classifier emerged as the most effective one, achieving an accuracy of 97.75%. Random Forest followed closely with 97.62%, while Logistic Regression showed the lowest accuracy at 93.72%. These high levels of accuracy illustrate the model’s reliability in predicting accident severity. Additionally, by integrating Intelligent Road Accident Prediction System (IRAPS) with GPS technology, the drivers could be notified of any potential alerts relating to adverse conditions. Henceforth, an attempt has been made to possibly make the journeys more safer.