This study reveals the simultaneous deep oxidation of benzene and toluene over the novel supported cobalt oxide catalyst derived from metal organic framework (MOF) over the almond shell based activated carbon. The performance of the fabricated catalyst was evaluated under the various operating conditions including oxidation temperature, initial concentration of benzene and toluene. The maximum conversion of benzene and toluene were also measured to be 89.74 % and 82.37 %, respectively. The sample morphology was studied by applying XRD, FESEM, BET and TGA analysis. The characterization tests indicated that the well dispersed spherical nano-supported catalyst was synthesized with size of less than 40 nm. To the best of our knowledge, the computational fluid dynamics (CFD) analysis incorporated with artificial neural network (ANN) was also studied for modeling the deep catalytic oxidation over the prepared sample. The modeling involved with the three dimensional analysis of polluted air flow through of a tubular micro-reactor axial inlet and outlet. The computational fluid dynamics was coded by adopting COMSOL Multiphysics to model the catalytic conversion of volatile organic compounds (VOCs) inside the porous media. The kinetic modeling was also conducted by using three-layer ANN to determine the reaction rates while the reaction temperature, initial concentration of benzene and toluene were considered as the input variables of network. The reaction rates were calculated by a non-linear feed-forward network with 5 neurons and log-sigmoid function in the hidden layer while the correlation coefficient was achieved to be 0.99. The validation of CFD model was accomplished which showed the appropriate matching between the experimental data and model achievements. Therefore, the developed intelligent hybrid model (CFD-ANN) in the offered investigation can be a useful tool for studying the fluid dynamics of VOCs oxidation over the nano-catalyst under the different operating conditions. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.