Evaluating the hot mix asphalt (HMA) expected performance is one of the significant aspects of highways research. Dynamic modulus (E*) presents itself as a fundamental mechanistic property that is one of the primary inputs for mechanistic-empirical models for pavements design. Unfortunately, E* testing is an expensive and complicated task that requires advanced testing equipment. Moreover, a significant source of difficulty in E* modeling is that many of the factors of variation in the HMA mixture components and testing conditions significantly influence the predicted values. For each laboratory practice, a vast number of mixes are required to estimate the E* accurately. This study aims to extend the knowledge/practice of other laboratories to a target one in order to reduce the laboratory effort required for E* determination while attaining accurate E* prediction. Therefore, the transfer learning solution using deep learning (DL) technology is adopted for the problem. By transfer learning, instead of starting the learning process from scratch, previous learnings that have been gained when solving a similar problem is used. A deep convolution neural networks (DCNNs) technique, which incorporates a stack of six convolution blocks, is newly adapted for that purpose. Pre-trained DCNNs are constructed using a large data set that comes from different sources to constitute cumulative learning. The constructed pre-trained DCNNs aim to dramatically reduce the effort elsewhere (target lab) when it comes to the E* prediction problem. Then, a laboratory effort reduction justification is investigated through fine toning the constructed pre-trained DCNNs using a limited amount of the target lab data. The performance of the proposed DCNNs is evaluated using representative statistical performance indicators and compared with well-known predictive models (e.g., gbased Witczak 1-37A, G,d-based Witczak 1-40D and G-based Hirsch models). The proposed methodology proves itself as an excellent tool for the E* prediction compared with the other models. Moreover, it could preserve its accurate performance with less data input using the transferred learning from the previous phase of the solution.