In recent years, applying quantitative risk analysis in Road cargo transport (RCT) has yielded successful results in assessing risks associated with industrial industries. Recent studies have highlighted the significance of different modes of transportation (such as roads, rails, pipelines, and inland waterways)for hazardous materials, emphasizing their role in determining risk levels. Among these modes, road transport via trucks stands out as a crucial component for economic development and is commonly employed for transporting various types of cargo. To effectively evaluate the risk level of a given activity within RCT, it is essential to determine the severity index for each potential situation that may arise during the transportation process. In this context, this research aims to investigate and calculate the risks associated with RCT by employing Bayesian Networks. Computational models were implemented using Bayesian Network software, and data input was carried out using Microsoft Excel®spreadsheets. The methodology employed for this study entailed field research with the involvement of experts and academic sources, along with the utilization of a systematic literature review. Additionally, the Delphi technique was applied, followed by a survey. The results pointed that the proposed model could effectively aid in identifying the level of risk involved in RCT operations across different scenarios. Furthermore, this model enables managers to evaluate the probability of one or more risk factors occurring during operations, facilitating the implementation of more efficient mitigation measures.