Maintaining optimal control of heating boiler systems using intelligent systems poses a significant challenge due to inherent nonlinearities, time delays, and unpredictable variations in fuel quality and thermal load. Conventional fuzzy logic controllers, although effective under steady-state conditions, often fail to deliver robust performance when subjected to abrupt parameter fluctuations. To address this limitation, this study proposes a novel hybrid quantum-fuzzy inference framework that enhances the adaptability and robustness of intelligent control systems for heating boiler automation. The approach integrates core quantum computing principles—specifically, superposition and probabilistic amplitude processing—with fuzzy rule-based logic to dynamically activate and generalize across a predefined set of control rules. A self-organizing and adaptive knowledge base is developed in real time, allowing the control logic to restructure itself in response to disturbances such as ±25% fluctuations in fuel flow rate, up to 30% variations in thermal demand, and 5–8 second measurement delays. The proposed control model is developed and validated using MATLAB/Simulink on a nonlinear heating boiler plant subjected to realistic operational disturbances, including random fuel composition. Simulation results indicate that the hybrid system achieves up to 36% improvement in control stability, 30% faster response time, and 22% reduction in energy consumption compared to conventional fuzzy systems. These findings demonstrate that the quantum-fuzzy approach is a promising solution for robust and energy-efficient automation of nonlinear thermal energy conversion systems, and can be generalized to other complex industrial processes requiring adaptive intelligent control.