This paper presents a methodology for maintenance and rehabilitation strategies for low volume roads based on developed pavement distress models. Saudi Arabia has made huge investments in constructing a large road network. To sustain this network, periodic evaluation and timely maintenance to keep the network operating are necessary. Historical data of pavement distress on low volume urban roads in five cities were collected--Riyadh, Jeddah, Dammam, AL-Madinah, and Jazan. These data were processed and analyzed, and the results have been employed to generate prediction models for pavement distress for the Saudi Urban Road Network (SURN) in order to develop the current approach. A sigmoid function was found to be the best fit for the data. Six prediction models for different pavement distress type have been developed. Maintenance and rehabilitation strategies have been proposed as applications of these prediction models.