The article addresses the shortcomings of traditional methods used for training neural networks in management systems and emphasizes the importance of exploring the capabilities of the neuro-control method with a predictive function. This method was analyzed using the example of an adaptive air control system within a liquid fuel combustion system of a ship’s steam boiler. The neural network training and the modeling of the control system were performed using the Neural Network Toolbox in Matlab, showcasing the advanced features of this platform. The results of the analysis of transition processes demonstrated that the neural network control system successfully meets expected performance criteria. Furthermore, the system showed strong adaptability to changes in the operating modes of the steam boiler, confirming the efficiency of neural control with a predictive function in dynamic conditions. This indicates that such neuro-control methods can significantly improve the stability, precision, and overall performance of complex industrial systems.