In this study, piston, exhaust, and intake valves of a diesel engine were coated with tungsten carbide in 300 mu m thickness using the high-velocity oxygen fuel coating method. Mathematical modeling of coated and uncoated (standard) engines was performed using the artificial neural network (ANN). The purpose of this study was to decrease the number of test repetitions and reduce the test cost by performing mathematical modeling of engines through the ANN. The results obtained from the tests were input in the ANN, and values of the engines at all revolutions per minute values were estimated. The results of the tests were compared to results obtained from the ANN, and they were observed to be consistent with each other. As a result of thermal barrier coating, specific fuel consumption, hydrocarbon, carbon monoxide, and smoke density values of the diesel engine decreased, whereas NOx and exhaust gas temperature increased. Furthermore, results obtained by applying mathematical modeling through the ANN reduced the number of test repetitions. Thus, it was understood that time, fuel consumption, and labor loss would be saved.