An innovative approach for identifying dozy drivers by integrating PERCLOS (Percentage of Eye Closure) analysis with advanced computer vision algorithms. The system continuously monitors real-time eyelid closure patterns using a webcam installed and provides prompt alerts when elevated PERCLOS levels indicate doziness. This non-intrusive methodology represents a significant leap forward in road safety, offering a reliable early detection indicator for dozy driving. The technology’s performance is underpinned by empirical research validating PERCLOS as a precise and effective measure for early dozy driver detection. However, the system may face challenges such as variability in lighting conditions, individual differences in blink patterns, and the accuracy of detection across diverse driving environments. Despite these challenges, the system’s real-time capabilities make it adaptable and effective across various conditions, helping mitigate fatigue-related accidents. By proactively addressing the issue of dozy driving, this cutting-edge system has the capacity to substantially reduce number of accidents, enhance transportation safety, and ultimately save lives. The real-time capabilities of system, coupled with its capacity ro adapt to a variety of driving situations, underscore its pivotal role in mitigating fatigue-related accidents. Future studies and advancements should also rely on refining these algorithms to address the mentioned challenges and further enhance accuracy and reliability. We conclude that it would be more effective to design a driver sleepiness detection system employing accurate approaches and that doing so is strongly advised.