Pavement Management Systems (PMS) are used by transportation agencies to develop maintenance and rehabilitation programs for the pavement network under their jurisdictions. PMS have prediction performance models to forecast the future condition of the pavement network; these models can be deterministic or probabilistic. Deterministic models are commonly used in PMS, but they do not consider the uncertainty in forecasting pavement performance. This paper presents an approach to develop probabilistic-based pavement performance curves (PBPPCs) to address the variability of the parameters involved in pavement performance predictions. PBPPCs are based on the pavement performance equations used by the Metropolitan Transportation Commission (MTC) in the San Francisco Bay Area in California. The application of PBPPCs into pavement management analyses provides a comparison of treatment and budget needs for a range of possible pavement performance scenarios. PBPPCs also allow transportation agencies be aware of the impact of possible pavement performance trends when allocating funds to preserve the pavement network in a state-of-good repair