Abstract: Road accidents are a major public safety issue because they kill 17 people every hour in India. We need to find good ways to stop accidents right away because each death is linked to many injuries of varying severity. Accurate forecasting is essential for transportation safety management due to the concerning increase in daily incidents attributed to the rapid rise in vehicle numbers. This study develops an accident prediction model utilising data mining techniques, particularly the K-Nearest Neighbours (KNN) Classifier and Support Vector Machines (SVM), and ensembles both results to increase the accuracy. The study takes into account the environment, the condition of the roads, and the amount of traffic. By spotting trends and predicting how often accidents will happen, this method may help government agencies, transportation departments, and NGOs focus their safety measures. Contractors, public works agencies, and automakers may also use the results to make safety improvements and better infrastructure design. The proposed forecasting technique illustrates how machine learning methodologies can improve road safety by reducing accident frequencies.