A COSMO-RS descriptor (Sσ-profile) has been used in quantitative structure–property relationship (QSPR) studies by a neural network (NN) for the prediction of empirical solvent polarity ENT scale of neat ionic liquids (ILs) and their mixtures with organic solvents. Sσ-profile is a two-dimensional quantum chemical parameter which quantifies the polar electronic charge of chemical structures on the polarity (σ) scale. Firstly, a radial basis neural network exact fit (RBNN) is successfully optimized for the prediction of ENT, the solvatochromic parameter of a wide variety of neat organic solvents and ILs, including imidazolium, pyridinium, ammonium, phosphonium and pyrrolidinium families, solely using the Sσ-profile of individual molecules and ions. Subsequently, a quantitative structure–activity map (QSAM), a new concept recently developed, is proposed as a valuable tool for the molecular understanding of IL polarity, by relating the ENT polarity parameter to the electronic structure of cations and anions given by quantum-chemical COSMO-RS calculations. Finally, based on the additive character of the Sσ-profile descriptor, we propose to simulate the mixture of IL–organic solvents by the estimation of the SMixtureσ-profile descriptor, defined as the weighted mean of the Sσ-profile values of the components. Then, the ENT parameters for binary solvent mixtures, including ILs, are accurately predicted using the SMixtureσ-profile values from the RBNN model previously developed for pure solvents. As result, we obtain a unique neural network tool to simulate, with similar reliability, the ENT polarity of a wide variety of pure ILs as well as their mixtures with organic solvents, which exhibit significant positive and negative deviations from ideality.