One of the key elements that performs an essential role in the success of an underbalanced drilling operation is the precise prediction of bottom-hole circulating pressure, especially when aerated mud is employed. Employment of complex two-phase flow correlations is the common procedure to predict the multiphase flow pressure gradient. Lack of generality and relatively big errors are the practical problems of such methods threatening the whole underbalanced condition of the operation. In this study, genetic algorithm is used in combination with the adaptive network-based fuzzy inference system to model the case with the optimal cluster center's range of influence for each of the data dimensions. The substantial improvement in the model performance and achievement of a MSE of .0015 showed that the stated combination performs much more accurately than the conventional methods.