In many countries throughout the world, driver sleepiness is a major cause of traffic accidents. Several driver sleepiness detecting technologies have been presented in recent years to help alleviate this issue. The OpenCV (Open-source Computer Vision) algorithm is used in this work to introduce a unique method for detecting driver sleepiness. To record real-time video frames of the driver's face, the suggested system makes use of a camera installed inside the car. Applying the OpenCV method, face characteristics are analyzed to look for indicators of sleepiness. The method consists of a number of steps, including face detection, facial landmark identification, and eye tracking. The driver's face is found in the video frames that were collected using a cascade classifier in the face detection stage. In the following step, a trained facial landmark recognition model is used to identify face landmarks. The technology analyzes the driver's eye closure patterns, a key sign of fatigue, by following the movement of particular facial markers. Extensive testing was done on a large dataset of drivers who displayed different levels of tiredness in order to assess how well the suggested system performed. The outcomes illustrate the system's efficacy in precisely and reliably identifying driver sleepiness with high recall rates. Through early notifications to sleepy drivers and the prevention of accidents brought on by driver weariness, the driver drowsiness detection system utilizing the OpenCV algorithm described in this research has the potential to improve road safety. The device may be integrated into current cars or added to advanced driver assistance systems (ADAS) to increase general road safety and lower the likelihood of accidents brought on by drowsy driving.