Road maintenance plays a vital role in maintaining road conditions and ensuring safety, especially in a country with an extensive road network like China. To accurately predict pavement performance, optimize maintenance strategy, reduce cost and improve road efficiency, the paper systematically combed and evaluated the prediction model of pavement performance. Firstly, the importance of pavement maintenance and the background of pavement maintenance performance prediction model are described, and explicit models (mechanical-empirical model, stochastic process, time series analysis) and machine learning models (regression analysis, support vector machine, integrated learning, artificial neural network, deep learning) are introduced respectively. The basic principle, representative study, advantages and disadvantages of each model are introduced in detail. Comparative analysis shows that the traditional explicit model is simple and effective, easy to explain, but difficult to deal with complex nonlinear problems; Machine learning models, especially deep learning models, have obvious advantages in dealing with complex nonlinear problems and large-scale data, but they are expensive to compute and poor in interpretation. The paper further summarizes the application of different models and puts forward that the future research direction should pay attention to the complementary advantages of models and the development of hybrid models, to improve the accuracy and efficiency of pavement performance prediction.