The rising number of road injuries is one of the most pressing challenges facing the world today. One of the main causes of traffic accidents were unsafe and inattentive driving. Drowsiness or a loss of focus on the part of the driver is believed to be a significant factor in such incidents. Driver drowsiness tracking research can assist in the reduction of road accidents.This journal presents a good approach for applying a driver's sleepiness alarm system that uses Machine Learning and Deep Learning techniques to identify and track the driver's yawning and sleepiness. For face detection and recognition, the device utilises the Histogram Centred Gradient (HOG) function descriptor, which is widely used in image processing. The SVM is then used to determine if the image being identified is a face or not. It also checks the driver's Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) up to a certain countable frames to see whether he or she is sleepy or yawning. Since the driver's drowsiness or fatigue is proportional to the number of hours spent behind the wheel, an extra element for changing theface and mouth reference frames have been added. This increases the sensitivity to detect drowsiness. This also necessitates the introduction of face recognition so that each driver can be tracked individually.This Project aims to provide a Driver Alert System consisting of three sections Face Recognition to unlock vehicle, traffic light detection and Drowsiness alert system.