A dynamic auto regression model based on the time series analysis with Kalman filter was proposed for pavement condition prediction. Existing prediction models could not be applied to Chinese airports due to the incomplete monitoring data and the complexity to be updated. The time series model was first established based on the pavement condition index (PCI) data of the airport in eastern China. Then Kalman filter algorithm was utilized to update the models. By the comparison with the actual monitoring data, the prediction models are proven to be reliable in Chinese airports. The predictions of the dynamic auto regression model are more accurate than the auto regression model despite the incomplete monitoring data.