Driver drowsiness is a critical factor contributing to road accidents worldwide. The accidents can be prevented if warning is provided on time to the drowsy driver. This research proposes a deep learning-based approach for detection of drowsiness in drivers. The system leverages computer vision techniques, specifically the calculation of Eye Aspect Ratio (EAR), to monitor and detect signs of drowsiness by analyzing facial landmarks. EAR is computed to determine eye closure patterns indicative of drowsiness. To enhance accuracy, a method for detecting sunglasses is integrated in the proposed system by utilizing Hue, Saturation and Value (HSV) color space and color masks to identify regions obstructing the eyes. Upon detecting drowsiness, the system triggers visual and auditory alerts to notify the driver, promoting timely intervention for safer driving. Experimental results demonstrate the system’s effectiveness in various lighting conditions and validate its ability to differentiate between drowsy and alert states with high accuracy. The proposed approach not only addresses the challenge of detecting drowsiness in diverse conditions but also contributes to mitigating road accidents caused by driver fatigue, thereby enhancing overall road safety.