The objective of this paper is to analyze and predict the level of risk in construction projects using Artificial Neural Networks. The objective of this paper, therefore, is to determine those factors that dictate the level of risk within the construction industry, as it is evident that the construction industries are susceptible to a number of probable risk factors likely to affect the timeline and overall cost of a project, including, but not limited to, the financial position of an organization, experience within the management team, legal aspects, environmental conditions, project complexity, time, and the quality of building resources. Data was collected from 109 construction projects, which were then processed and input into the ANN model to identify the relationships between these factors and the risk level. The results of the analysis show that some factors strongly influence the risk level of the project, and the ANN model has a high degree of accuracy in the prediction of risk levels. The findings of the study confirm that ANN can serve as a potential tool in controlling and forecasting various risks associated with construction projects by facilitating the project managers to make the right decisions at the appropriate time, hence enhancing management efficiency and reducing losses to the minimum.