New quantitative structure-property relationships (QSPRs) for estimating dynamic viscosity (eta) of pure ionic liquids (ILs) as a function of temperature and group contributions (GCs) are presented and evaluated. The correlations were established using three common machine learning algorithms (stepwise multiple linear regression, feed-forward artificial neural network, and least-squares support vector machine) on the basis of the largest database reported thus far, including the data for 2068 distinct ILs (3236 data sets and 22 268 data points). The GC scheme as well as two-stage modeling protocol (representing the property using separate reference term and temperature correction models) were applied consistently with the previous contribution [Ind. Eng. Chem. Res. 2019, 58, 5322-5338]. Standard internal and external validation techniques (such as, K-fold cross-validation, y-scrambling, "hold-out" testing, and the Williams plot) were adopted to select the best set of GCs, hence statistically the most significant model. The impact of the chemical structure of both cations and anions (as well as their combination) on the accuracy of prediction and classification (with respect to the order of magnitude of eta) is analyzed in detail. The obtained models are compared with other methods reported in the literature. In particular, a broad comparison of the finally recommended model with the QSPR, employing descriptors derived from molecular geometry and charge distribution [J. Phys. Chem. B 2011, 115, 300-309] is given.