The infinite dilution molar conductivity (λB∞) that represents the interactions between ions and solvent molecules is an important transfer property for the utilization of ionic liquids (ILs) in electrochemical applications. However, employing the quantitative structure–property relationship (QSPR) model to predict the λB∞ of unconventional ions remains to be explored. In this work, new λB∞-QSPR models were developed to predict the λB∞ of ions in aqueous solutions by using multiple linear regression (MLR) and stepwise linear regression (SLR) methods based on the molecular descriptors obtained by COSMO-SAC. A total of 132 cations and 158 anions data points at different temperatures were collected and tested. The results showed that the determination coefficients (R2) of the QSPR model using MLR for cations and anions were 0.9515 and 0.9411, and the average absolute relative deviations (AARD) were 6.79% and 10.42%, respectively, indicating that the proposed λB∞-QSPR model was excellent at predicting λB∞. Moreover, R2 of the QSPR model using SLR for cations and anions were 0.9450 and 0.9406, and AARD were 7.10% and 10.19%, respectively, implying that the λB∞-QSPR model with fewer descriptors could also predict λB∞ satisfactorily. We envisage the established λB∞-QSPR models provide an available method for obtaining λB∞ of ions in aqueous solutions.