Traffic accidents continue to be a major problem around the world. Despite numerous advancements in safety measures, existing policies often adopt a uniform approach to tackle this issue. The problem with this approach is that it fails to consider how age impacts driver behaviors, accident likelihood, and risks that various age groups face. The age of the driver impacts their decision-making, risk-taking behaviors, and overall driving. Young drivers often tend to drive aggressively and at speed, while older drivers struggle due to decreased cognitive ability and slower response times. Environmental conditions such as severe weather, low visibility, and insufficient road infrastructure further affect the drivers. This research aims to bridge the research gap by examining the interactions between traffic accidents caused by drivers from different age groups and accident risk factors, with a primary focus on environmental factors. This research delves into the influence of crucial risk factors, including road conditions, lighting conditions, and surface types, on traffic accidents that occur within each distinct age group. The findings of the research provide new insights into specific vulnerabilities of drivers of different age groups. The proposed method with the decision tree achieves an accuracy of 85%, outperforming various other classification algorithms. The proposed approach demonstrated better interpretability and accuracy, making it a viable solution for age-specific road safety approaches.