Drowsiness is a critical issue that contributes to a significant number of accidents in various scenarios, such as driving and hazardous work environments. Existing drowsiness detection projects often rely on subjective measures and single modality detection, leading to limited accuracy and applicability. This research proposes a drowsiness detection system that employs deep neural networks and machine learning-based object detection techniques to overcome these limitations. The ability of the recent drowsiness detection systems to reliably and impartially detect drowsiness is restricted. The proposed model uses computer vision and machine learning algorithms to identify drivers’ drowsiness based on facial attributes like eye movement monitoring. The model aims to improve the accuracy and reliability of drowsiness detection by combining multiple modalities. The implementation includes using the Keras library, which is required for a convolutional neural network (CNN) architecture. The model is trained on a customized dataset of facial images with open or closed eyes labels. The CNN discovers the complex relationships and features from the data, classifying drowsiness critically. The proposed drowsiness detection system’s results demonstrate an optimistic accuracy of 98.88%. The system signals real-time alerts when the drowsiness in the behaviour of the driver is caught, potentially averting accidents and enhancing safety. This technique suggests an accurate and trustworthy approach for detecting drowsiness in different domains, including driving and unsafe work environments, with 98.88% accuracy. This system can be a valuable means for improving safety and controlling the accidents caused by driver drowsiness.