The experiment employs 45 steel and Fe45 as the base material and cladding powder, respectively, to develop three factors and five levels of the test scheme, and evaluate the cladding layer's macroscopic size, to solve the challenge that the cladding layer morphology is difficult to control in a laser cladding process, First, the backpropagation (BP) neural network's initial value was optimized using a genetic algorithm (GA), and GA-BP neural network model was developed. The laser process parameters were taken as input and cladding layer morphology as output to train and test, and the prediction accuracy was examined. Second, the prediction model was developed using regression analysis, BP neural network, and GA-BP neural network, and compared with the actual cladding layer morphology. The findings demonstrate that the GA-BP neural network model's error optimized by the genetic algorithm was about 3%, the maximum error of the BP neural network prediction model was 7.38%, and the maximum error of the regression analysis prediction model is 15.5%. It can be seen from the comparison that the results of the GA-BP neural network prediction model are the closest to the actual. Thus, the GA-BP neural network prediction model has a certain regulating value for enhancing the quality of the cladding layer.