Utilizing machine learning for the prediction of road accidents is a promising area, and the methodologies that are being used for such purposes rely on historical and real-time data. Review of main steps in the road accident prediction process: data collection, data preprocessing, and implementation of models. Historical data includes those derived from traffic reports, weather conditions, and accident records. In this regard, real-time data sensors will provide immediate insights into traffic flow. While machine learning algorithms, including Support Vector Machines, Random Forest, and Neural Networks, have huge promise in predicting accidents quite accurately, data quality, biasedness, class imbalance, and variability across regions are the main issues that require attention. Moreover, real-time integration with traffic management systems involves high-speed processing of huge amounts of data, which is a tough challenge on the technical front. Results from a few studies showed that models like Random Forest and Neural Networks have high precision in accident prediction; therefore, further research is needed to overcome the existing limitations and make such systems more reliable and transparent for the decision-makers.