In the present work, a neural network (NN) model based on quantitative structure-viscosity relationship was implemented for predicting the shear viscosity of CO2-loaded and CO2-free aqueous amine solutions and their blends. A total of 1682 amine + CO, + water viscosity data sets for primary, secondary, and tertiary amines and 220 data points for further accuracy examinations were used. Molecular mechanic methods with CHARMM + CFF force fields were utilized in order to optimize, simulate, and extract the required molecular structure properties. Then, weighted nearest neighbor feature selection algorithm was used for selecting the most influencing descriptors, while cascade-forward NN (CFNN) model was applied for prediction purposes. For generality examinations, CO2-loaded aqueous systems of 3DMA1P(1) + EAE(2), MEA(1) + PZ(2), DMA2P(1) + MEA(2), DEAE(1) + PZ(2), and CO2 + pure water were used to find the solution viscosities, and comparisons were made against experimentations, which showed the quite robustness of the proposed model for the systemsm which were completely unaware of the trained model. Comparison between the values of average relative deviation of the NN model and the most important semiempirical viscosity models showed that CFNN model outperforms the other alternatives.