The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate and simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, is dependent on a number of parameters related to mix design and testing conditions. This study introduces a new model for predicting the rutting depth of asphalt mixtures using a deep learning technique – the convolution neural network (CNN). A database containing 10,000 data points from a comprehensive collection of HWTT results was used to develop a CNN-based machine learning prediction model. The model has been formulated in terms of known influencing mixture variables such as asphalt binder high-temperature performance grade, mixture type, aggregate size, aggregate gradation, asphalt content, total asphalt binder recycling content, and testing parameters, including testing temperature and number of wheel passes. The model can be used as a tool to estimate the rut depth in asphalt mixtures when laboratory testing is not feasible or for cost-saving, and pre-design trials.