Driver drowsiness detection and alert generating system is a form of artificial intelligence that practices machine learning algorithms to detect signs of drowsiness in drivers. This technology typically involves sensors such as cameras, microphones, and accelerometers that track the driver’s behaviours, such as head position, eye movement, and yawning, and then use machine learning algorithms to detect patterns of drowsiness. The technology can then alert the driver if they are at risk of dozing off, helping to reduce the risk of accidents or dangerous driving behaviour. A machinelearning approach to detect drowsiness in drivers using facial landmarks. The planned system uses a convolutional neural network (CNN) to observe the facial features of a driver in real-time and then compares them with a set of predefined features associated with drowsiness. The system can then alert the driver of their drowsiness by sounding an alarm or displaying a warning message. The planned method can be used in various applications, such as driver assistance systems, autonomous vehicle systems, and public safety systems. Lastly, we outline the issues that current systems confront and discuss the associated research prospects