Driver drowsiness is a critical issue contributing to road accidents worldwide. Traditional methods for detecting drowsiness in drivers primarily rely on physiological signals and facial recognition. However, these methods may not capture the nuanced emotional states of drivers, which can also be indicative of drowsiness. This paper presents a novel approach to driver drowsiness detection using machine learning techniques and emotion analysis. In experimental evaluations, our system demonstrated promising results in terms of accurately detecting drowsiness-related emotions in drivers. By integrating emotion analysis with traditional physiological indicators, our approach offers a more comprehensive and robust drowsiness detection system. This can lead to enhanced road safety by providing timely warnings to drowsy drivers, potentially preventing accidents and saving lives. Our research represents a significant step towards improving driver safety through the integration of emotion analysis and machine learning in drowsiness detection systems. Future work will focus on refining the model, exploring additional data sources, and integrating this technology into existing vehicle safety systems.