ABSTRACT: Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Every year, they increase the amounts of deaths and fatalities injuries globally. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this dangerous condition. The major problem of the developed algorithms is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and the time represent a critical condition that affects the reduction of traffic accidents. In recent years, with the progress of huge amount of data, computer vision technology and Machine Learning (ML) technology have been used in various applications due to their effectiveness and accuracy. Hence in this, A novel computer vision based effective real time driver yawn and fatigue detection approach using ml techniques is presented. The canny edge detection algorithm and sobel edge detector is employed to extract facial features such as eye closure and yawning. The ML algorithms such as K-nearest neighbour (KNN) and Support Vector Machine (SVM) are used to detect the driver fatigue and yawn in real time. This approach will achieve better results compared to earlier approaches.