Abstarct: In this work, the density of ionic liquids has been estimated using two methods that includes: coupling of group contribution method with artificial neural network (GCM-ANN) and group contribution method with support vector machine (GCM-SVM). The data set consists of 3107 experimental data points of density for a wide range of temperatures (293-414 K), pressures (0.1-65 MPa) and densities (869.21-2400 Kg.m-3) corresponding to 188 ionic liquids. The data set in ANN was randomly divided into three groups: training, validation and test set and the data set in SVM was divided into two groups: training and test set. We employ a total of 33 sub-structures plus temperature and pressure as input variables. After training and optimization of the ANN and SVM parameters, the performance of the model was investigated by the test set. The results obtained using ANN and SVM were compared with the experimental values. The results show that the chosen support vector machine and the group contribution method represent an excellent alternative for the estimation of the density of ionic liquids with acceptable accuracy (MSE=12.9525, R2=0.9996), for a wide range of temperatures and pressures.