Predicting the viscosity of ionic liquids (ILs) is crucial for their applications in chemical and related industries. In this study, a large data set of experimental viscosity data of ILs with a wide range of viscosity (7.83-142 000 cP), pressure (1-3000 bar), and temperature (258.15-395.32 K) are employed to build predictive models. The structures of cations and anions for 89 ILs are optimized, and the S sigma-profiles descriptors are calculated using the quantum chemistry method.Two new models are developed by using extreme learning machine (ELM) intelligence algorithm with the temperature, pressure, and a number of S sigma-profiles descriptors as input parameters. The coefficient of determination (R-2) and average absolute relative deviation (AARD %) of the total sets of the two predictive models are 0.982, 2.21% and 0.951, 4.10%, respectively. The results show that the two ELM models are reliable for predicting the viscosity of ILs.