In the recent era, accelerated expansion of industrialization and urbanization leads to the release of a large volume of effluent comprising of wide varieties of toxic pollutants. Amidst these components, oily hydrocarbon is one of the alarming toxic pollutants causing severe health hazards to ecological species. For the treatment of oily wastewater discharged from various sources, membrane technology is an established separation process to deliver clean water before disposal in accordance with government environmental regulations. However, one of the main limitations of membrane technology is its economic feasibility for the long-run of treating heavy pollution load because of membrane fouling, which gradually declines the permeate flux rate. Moreover, real difficulties lie in evaluating the foulants’ interaction with the membrane material, and henceforth, parameter manipulation, which is highly dependent on the in-depth chemistry associated with different unforeseen real-life consequences. Therefore, conventional mathematical models are often considered unsuitable for membrane separation processes as it does not incorporate any randomness of the system, but only dealing with known mass, energy and momentum balance. But stochastic models can proficiently handle real, complex system with inherent uncertainties, where one fails to predict any relation between the outcome and the input. Among these stochastic models, pattern mapping models such as Artificial Neural Network (ANN) is one of the most significant approach, which mainly relies on the experimental data and their accuracy level rather than developing any correlation in between the process parameters and outcome. However, one of the main intricacies involved with ANN is the unexpected occurrences of errors that predict unreliable output of the process. To compensate this limitation, research efforts has been directed toward the development of neuro-fuzzy hybrid models such as Adaptive neuro-fuzzy inference system (ANFIS) for modeling membrane separation process efficiently.