A new group contribution (GC) quantitative structure-property relationship (QSPR) for estimating density (rho) of pure ionic liquids (ILs) as a function of temperature (T) and pressure (p) is developed on the basis of the most comprehensive collection of volumetric data reported so far (in total 41 250 data points, deposited for 2267 ILs from diverse chemical families). The model was established based on a carefully revised, evaluated, and reduced data set, whereas the adopted GC methodology follows the approach proposed previously [Ind. Eng. Chem. Res. 2012, 51, 591-604]. However, a novel approach is proposed to model both temperature and pressure dependence. The idea consist of an independent representation of reference density rho(0) at T-0 = 298.15 K and rho(0) = 0.1 MPa and dimensionless correction f(T, P) equivalent to (rho)(T, p)/rho(0) for other conditions of temperature and pressure. Three common machine learning algorithms are employed to represent the quantitative structure property relationship between the studied property end points, GCs, T, and p, namely, multiple linear regression, feed-forward artificial neural network, and least-squares support vector machine. On the basis of detailed statistical analysis of the resulting models, including both internal and external stability checks by means of common statistical procedures such as cross-validation, y-scrambling, and "hold-out" testing, the final model is selected and recommended. An impact of type of cation and anion of the accuracy of calculations is highlighted and discussed. Performance of the new model is finally demonstrated by comparing it with similar methods published recently in the literature.