Modular construction (MC) represents an innovative approach to optimize construction processes, while also offering significant gains in terms of efficiency and sustainability. A crucial aspect that is often neglected is the transportation cost of the materials used in modular construction projects. Therefore, there is a lack of data regarding this issue, so this study introduces a deep learning-based approach for forecasting the transportation costs of other type of goods, aiming to later optimize logistical planning and budgeting in MC. The increasingly complex nature of the market requires new and innovative business solutions, which is why our cost prediction model aims to bridge the gap in relevant information in the civil construction domain. Evaluating the feasibility of our proposed algorithm, we obtained a MAPE value of 25.8%, which outperforms the LGBM Regressor that generated a MAPE value of 30.5%. These results indicate that the proposed method has potential to produce superior outcomes when compared to the LGBM Regressor, a very powerful tree-based algorithm. Additionally, it is possible to conclude that our tool is capable of providing good results, enabling informed decision making and resource management in this highly complex and dynamic sector.