Ionic liquids (ILs) are ionic compounds with low melting points that can be designed to be used in an extensive set of commercial and industrial applications. However, the design of ILs is limited by the quantity and quality of the available data in the literature; therefore, the estimation of physicochemical properties of ILs by computational methods is a promising way of solving this problem, since it provides approximations of the real values, resulting in savings in both time and money. We studied two data sets of 281 and 134 liquids based on the molecule imidazole that were analyzed with QSPR techniques. This paper presents a software architecture that uses clustering techniques to improve the robustness of estimation models of the melting point of ILs. These results indicate an error of 6.25% in the previously unmodeled data set and an error of 4.43% in the second data set. We have an improvement with the second data set of 1.81% over the last results previously found.