This study aims at indicating the capability of a state-of-the-art computational intelligence approach for predicting pseudosteady flux and pseudosteady fouling at different operating condition (temperature (T), transmembrane pressure (TMP), cross-flow velocity (CFV), and feed pH) as well as for permeate flux decline at the mentioned operational conditions with processing filtration time. To train and test these models, the experimental data collected during the polyacrylonitrile (PAN) UF process to treat the oily wastewater of a Tehran refinery have been used. The proposed method utilizes a least-squares support vector machine (LSSVM) to carry out nonlinear modeling. The genetic algorithm (GA) was employed to tune the optimal model parameters. GA-LSSVM has the competence of describing the nonlinear behavior. The accuracy of the proposed GA-LSSVM models is very satisfactory and quantified by statistical parameters. Finally, the results obtained by implementing various sensitivity analysis techniques portrayed that T and TMP have the most significant influence on pseudosteady flux and pseudosteady fouling, correspondingly, in comparison with other factors involved in the addressed treatment process.