Feeling sleepy while driving is one of the leading reasons behind road accidents around the world. That’s why detecting drowsiness early is so important for keeping people safe on the road. This paper takes a closer look at different ways to spot drowsiness in drivers, including methods based on computer vision, body signals, machine learning, and driving simulations. Among these, computer vision techniques focus on tracking facial features like blinking, yawning, and head movements to spot signs of tiredness. Physiological signal-based techniques include Electrooculogram, and heart rate variability, which are very accurate but intrusive and complex for real- world applications. Approaches from machine learning and deep learning, Convolutional Neural Network, demonstrate strong promise through real-time prediction that combines visual and behavioral indicators. Systems for detection are improved with the incorporation of simulation-based systems with signals derived from both vehicle-based and driver-monitoring ones within controlled environments.