Potholes are a type of obstructions on road that can damage vehicles, impair drivers’ safety, and also lead to traffic accidents. However, it is necessary to design an effective automated pothole detecting system to help the authorities in the timely maintenance and repair of roadways. So, in this survey, various image processing and object detection algorithms are discussed to automate pothole detection. Additionally, Machine Learning (ML), Deep Learning (DL), and Hybrid models are recommended for pothole detection to address the problems in real-time environments. Furthermore, DL algorithms are used to analyze complex image data and identify potholes by learning distinctive visual features. Then, ML algorithms are used to create the best possible model with the maximum prediction accuracy of pothole incidence. Then, in order to enhance the overall performance of road pothole identification, hybrid models were used to integrate two or more algorithms.