Nonlinear technological processes are characterized by strong dynamic coupling, multivariable interactions, and significant uncertainty caused by time-varying operating conditions and external disturbances. These characteristics substantially limit the performance of conventional control strategies, such as classical PID and fixed rule-based controllers, which often lack sufficient adaptability and robustness in complex industrial environments. Even conventional fuzzy controllers may exhibit degraded performance when operating regimes change rapidly or when multiple competing control actions must be evaluated simultaneously. Consequently, the development of advanced intelligent control approaches for nonlinear and uncertain systems remains a critical challenge in modern automation and control engineering. This paper proposes a quantum-inspired fuzzy inference-based intelligent control framework for nonlinear technological processes. The proposed approach integrates a fuzzy inference system as the core decision-making mechanism with quantum-inspired computational principles, including probabilistic state representation, parallel evaluation of alternative control actions, and adaptive weighting mechanisms. Unlike true quantum computation, the proposed method employs quantum-inspired concepts at the algorithmic level to enhance decision flexibility and robustness while remaining fully compatible with classical real-time control platforms. Fuzzy inference enables the incorporation of expert knowledge and linguistic uncertainty, whereas the quantum-inspired structure allows simultaneous assessment of multiple control scenarios within each control cycle. The controller is implemented in a closed-loop architecture and coupled with a dynamic nonlinear process model. Real-time process measurements are used to adapt inference parameters online, while quantum-inspired weighting reinforces favorable control actions and suppresses suboptimal ones. Simulation results demonstrate faster transient response, improved disturbance rejection, and higher operational efficiency compared to classical PID and conventional fuzzy controllers. The proposed methodology is applicable to a wide range of automation and control systems, including energy conversion and thermal processing units.