Road accidents have been a significant concern in India, resulting in yearly fatalities. Pedestrians are among the most vulnerable road users, especially vulnerable to severe injuries and fatalities. This paper aims to examine the traffic accident frequency in road accidents in India, highlighting the contributing factors, consequences, and potential interventions to improve pedestrian safety. To comprehensively understand this critical issue, the study draws on available data, statistical analysis, and insights from relevant literature. The findings highlight the need for effective measures in India to reduce pedestrian accidents and create safer road environments. This chapter studies the use of machine learning algorithms to forecast the frequency of pedestrian accidents. The M5P model tree, linear regression, and SVMreg model were the three models utilized with the help of the machine learning tool Weka (Waikato environment for knowledge analysis), bearing in mind the promising results of non-parametric accident frequency prediction models mentioned in the past studies.