Potholes act as indicators of poor road maintenance, revealing deeper structural concerns. Vehicle encounters with potholes not only lead to uncomfortable rides but also result in potential damage to wheels, tires and suspension, causing heavy repair expenses. Driver drowsiness is another serious issue that poses a grave threat to road safety, as it can lead to accidents and injuries. The integration of sensor data provides additional contextual information about vehicle dynamics and road conditions, further enhancing the accuracy and reliability of pothole detection. The next section is to prevent drowsiness by using the Facial Landmarks Detection algorithm for detecting the face of the driver and thereby detecting the drowsiness signs. To address these challenges (Bucko et al. in Internet Technol Lett 3:e156, 2022 [1]), this paper aims to develop a mobile application that incorporates machine learning algorithms to detect potholes on roads and prevent accidents caused by driver drowsiness. The proposed system is a portable and accessible solution for enhancing road safety by combining machine learning and image processing techniques to overcome the pothole and drowsiness.