Ionic liquids (ILs) have shown remarkable potential for applications in separation, such as extractive distillation and liquid-liquid extraction. Crucial to these applications is the estimation of a significant property of the ILs which is the infinite dilution activity coefficient (IDAC) of different solutes in ILs. In this context, the present paper aims to model IDAC of 17 solutes in 44 imidazolium ILs using 2666 experimental data points gathered from the literature and based on support vector machine for the regression (SVMr) learning algorithm. Two models are developed, one based on SVMr and the other one based on dragonfly algorithm (DA) associated with SVMr. Both models consider the same set of predictive variables which are the temperature, the molecular weight of solute and solvent, and five conductor-like screening models for real solvents (COSMO-RS) sigma-profile descriptors related to the solute and IL. The DA is applied for optimization of SVMr hyper-parameters. The results show the superiority of the DA-SVMr model demonstrated by its correlation coefficient (R) and root mean square error values of 0.996 and 0.170, respectively.