Several studies involving the implementation of artificial neural network (ANN) technology for process design, monitoring, and control are under active research. This new technology has shown great potential in advancing chemical processes through the development of digital twins and smart factories. In joining this race, the current study explores the capability of physics-based modeling (CFD) and artificial neural networks for advanced process data visualization. Here, 20 CFD simulations of a multi-tubular reactor equipped with a Zn-Fe-Cr catalyst for synthesizing butadiene were executed. The simulation result was extracted as 3-D data with XYZ coordinates and imported into a python-based DNN model for training and cross-validation. An accuracy of 99.2% was obtained from the ANN surrogate model. The trained model was used to predict 3D data in terms of the process temperature, concentration, etc. The 3D data was then imported into a Paraview® VTK for detailed virtualization. Cross-sectional, longitudinal, and radial distribution of the various process variables, such as concentration profiles and pressure contour, were effectively visualized. A graphic user interface was further developed using Python for real-time visualization of the equipment. This implementation is analogous to the digital twin and is employable for online system optimization, high accuracy, low computational cost, and seamlessly integrable 3D real-time visualization system design for efficient, quick, and easy plant decision-making.