Electroless nickel boron (ENB) coatings have received widespread attention due to some important properties such as hardness, wear resistance, and low coefficient of friction. The present work is directed toward the development of a nickel boron coating to obtain the maximum coating thickness using artificial intelligence, i.e., artificial neural network (ANN) and metaheuristic algorithm. Different researchers have adopted different schemes and deposition parameters. So, a wide range of data is available for the coatings in the literature. All these data were trained using ANN with nickel chloride, sodium hydroxide, sodium borohydride, ethylenediamine, lead nitrate, bath temperature, plating time, and pH as process parameters. The coating thickness was maximized using genetic algorithm (GA). The optimal thickness obtained was 27 µm. Thus, the combined ANN-GA model could be successfully utilized to predict a bath composition and deposition conditions to obtain higher coating thickness.