Membrane fouling as a major concern in development and optimization of membrane bioreactor (MBR) technologies has been the focus of numerous engineering and research investigations. Considering the complexity of membrane fouling occurrence, mathematical modelling techniques have been progressively proposed to forecast this phenomenon for optimizing MBR performance. A majority of the models are not reliable and accurate enough in terms of theoretical and practical prospects. In this research work, smart methods including artificial neural network (ANN), gene expression programming (GEP), and least square support vector machine (LSSVM) are suggested to avoid utilization of complex modelling methodologies and costly and time-consuming measurements. The developed models relate fouling resistance to key parameters such as permeate flux, temperature, and transmembrane pressure. To enhance the performance of conventional connectionist tools, particle swarm optimization (PSO) algorithm with global optima is utilized. This study aims to simulate the MBR efficiency by calculating membrane fouling resistance. The performance of the smart models is evaluated based on the mean squared error (MSE), maximum absolute percentage error (MAAPE), minimum absolute percentage error (MIAPE), and coefficient of determination (R-2). The results reveal that the developed LSSVM tool has the lowest MSE (0.0002), MAAPE (3.18), and MIAPE (0.01), and the highest R-2 (0.99) in the testing phase. The transmembrane pressure and permeate flux are the most important parameters affecting the membrane fouling resistance. This study can help to obtain a better understanding of membrane fouling process to achieve optimal conditions for MBR systems in terms of design, operation, and optimization prospects. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.