Rutting prediction models are essential elements of efficient pavement management systems. Accuracy of commonly used predictive models necessitates knowledge of the input parameters that they incorporate and local calibration of the model coefficients. This study aims at developing a rutting prediction model that incorporates a limited number of inputs, yet is able to accommodate, with sufficient generalisation abilities, to the data scarcity and resource limitations in developing countries. The prediction model is developed by employing deep neural network techniques (DNN) on data extracted from the Long-Term Pavement Performance (LTPP) database. The predictive capability of the DNN model is compared to those of the state-of-the-practice and a multivariate linear regression model fitted using the same dataset. It is found that the DNN rutting prediction model features enhanced predictive power compared to commonly used models in the literature. In addition to predicting pavement rutting, the developed model is further utilised to assess and rank the relative impact of the different model inputs on rutting. The sensitivity analysis results confirm the high influence of traffic and climatic conditions. Moreover, generic family rutting predictive curves corresponding to specific traffic, climate, and performance combinations are developed to render rutting predictions available to all road agencies.