Predicting pavement rutting is associated with significant uncertainties that often lead to inefficient maintenance planning. The predictive performance of rutting models is exacerbated in local road agencies and developing countries that rely on generic and knowledge-based models which are typically unreliable if used without adaptation, validation, or calibration. This study aims at developing a probabilistic framework that employs Ensemble Kalman Filter (EnKF) techniques to update the parameters associated with generic rutting predictive models while accounting for the prevailing uncertainties. When coupled with a continuous influx of measured data, the EnKF framework sequentially updates the generic models and minimizes prediction errors in real-time. The robustness of the presented scheme is demonstrated through a numerical example, and its sensitivity to the use of different generic curves as starting points is examined. The results indicate that the EnKF framework improves the accuracy of rutting predictions by up to 60% and that accuracy remains within tolerable limits whilst varying the range of the uncertainty in the measurements or the initial states. The paper concludes with a discussion of how practitioners can integrate the outcomes of the presented framework to enact maintenance policies that minimize the financial cost at the project and network levels.