Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization. In this work, the qualitative analysis of the influence of cations and anions on the H2S solubility in ILs has been conducted. The results indicate that anions play an important role in determining the H2S solubility in ILs. Subsequently, two novel quantitative structure-property relationship (QSPR) models are developed based on charge distribution area (S sigma-profile) descriptors and an extreme learning machine (ELM) algorithm. To develop the QSPR models, a total of 1282 pieces of data belonging to 27 ILs are employed to validate the models. The average absolute relative deviation (AARD%) and coefficient of determination (R-2) of the two QSPR models of the entire data set are 3.73% and 0.998, as well as 3.80% and 0.997, respectively. These results suggest that the proposed QSPR models can be useful for the prediction of H2S solubility in ILs.