Construction is a critical sector in the economy, but it has several inefficiencies. Modular construction techniques appear to tackle this issue by building modules of the buildings in a factory-like environment and then shipping them to the construction site for assembly with other modules. However, this brings logistical issues, as the transportation of the very high-volume modules is very costly, as special transportation requires several mandatory extras, and these costs are hard to estimate, especially due to the lack of data. This study develops a deep learning model for transportation cost prediction in modular construction. Given the lack of data on module transportation, we use transfer learning techniques to use knowledge from a previous deep learning model that predicted the cost of raw material transportation. We tested the proposed methodology against three other strategies, and the proposed model using transfer learning achieved the best performance. However, there is room for improvement as the predictive error is still quite high. The novelty of our study lies in its novel application, which was never considered before. This study also contributes to practitioners, as it allows module designers/architects to create modules with cost-effective transportation.