Abstarct: Artificial neural network (ANN) and support vector machine (SVM) were successfully used for the modeling and prediction of viscosity of n-alkanes over a wide range of temperature and pressure. A large number of descxriptors were calculated with Dragon software and a subset of calculated descxriptors was selected from 18 classes of Dragon descxriptors with a stepwise multiple linear regression (MLR) as a feature selection technique. Four calculated and two experimental descxriptors, pressure and temperature, were selected as the most feasible descxriptors. The selected descxriptors were used as input nodes for generated 5-5-1 networks and support vector machine. The data set in ANN was divided into three data sets: training, prediction and test set and the data set in SVM was divided into two data set: training and test. 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.