Shielded metal arc welding is extensively used for fabrication, maintenance, and repair purposes of power plant welds. Flux's thermophysical, physicochemical, and high-temperature wetting behavior significantly influences the weld pool's properties. The coating fluxes were formulated using an extreme vertices design approach. The aim of this work is to conduct experiments and model the wettability properties, such as contact angle and work of adhesion, of the developed shielded metal arc welding (SMAW) coating flux using an artificial neural network (ANN) approach. Pellets were prepared to perform the experiments. The contact angle and work of adhesion were calculated using Image J software. The developed ANN model has been tested for its prediction capability and compared with regression analysis predictions. Results show that the ANN model of contact angle and work adhesion has improved root mean square error of 45.25 and 60.37%, respectively. The ANN model has shown better prediction accuracy than regression analysis.