The proliferation of road accidents attributed to driver drowsiness necessitates effective countermeasures for ensuring road safety. In response, this paper presents a comprehensive review and analysis of driver drowsiness detection systems. By integrating advanced technologies such as computer vision, machine learning, and physiological sensors, these systems aim to detect and mitigate the risks associated with drowsy driving. This review examines various methodologies employed in existing systems, including facial recognition, eye tracking, steering behavior analysis, and physiological signal monitoring. Furthermore, it analyzes the effectiveness of these techniques in real-world scenarios and their potential for integration into automotive safety systems. Additionally, the paper discusses challenges and future directions for enhancing the performance and reliability of driver drowsiness detection systems. By synthesizing existing research findings, this review provides valuable insights for researchers, practitioners, and policymakers working towards the development of proactive measures to combat drowsy driving and improve road safety