In the past two decades, flooding has been the most frequent natural disaster that has happened all over the world, accounting for more than 40% of all disasters that have occurred. This is the same story in the Philippines, bringing devastating damages from agriculture to infrastructure. Socio-economic damage is evident, which not only hinders the advancement in development but can also bring trauma and stress to the community. Due to this, the government is constantly providing mitigating measures to counter the effects of flooding and the flood itself. This includes the construction of different flood control projects all over the country. However, the cost of such projects cannot be simply estimated. This study aims to forecast the construction cost of flood control projects based on maximum flood level susceptibility, precipitation, and bearing capacity. A hybrid ANN-PSO was utilized as the prediction model to render the most optimal and reliable solution. With the aid of Garson’s algorithm, a sensitivity analysis was also conducted to level the weight importance of each variable in the network. Accordingly, the network demonstrated excellent performance, achieving a regression value of 0.99725. The MSE and MAPE, on the other hand, generated highly accurate predictions when compared to other models.