The chemical process industry frequently encounters uncertainties, disturbances, and varying operational demands, necessitating robust control strategies to ensure safety, efficiency, and optimal performance. In addition, the non-linearities inherent in chemical processes complicate control efforts, as system behavior can vary significantly under different operating conditions. This paper presents the development and application of a Fuzzy Multi-Model Control (FMMC) system designed to manage chemical processes with long time delays at various operating points. The Takagi-Sugeno (T-S) fuzzy modeling approach, where each fuzzy rule represents a local linear system, is integrated with Dynamic Sliding Mode Control (DSMC) to enhance robustness and control performance under these varying conditions. To ensure the accuracy of the system's dynamics and provide a solid foundation for the proposed control strategy, the modeling was validated using the mean squared error (MSE). The parameters of the proposed controller were determined using Particle Swarm Optimization techniques (PSO), which optimize the effectiveness of the controller. The controller employs fuzzy switching, resulting in a streamlined DSMC formulation that effectively adapts to changes in the dynamic process. The results of the thermal processes performed in real-time experiments showed that the proposed controller not only maintained control within the operational range but also reduced the impact of disturbances and uncertainties. Moreover, its performance was validated using the indices IAE, ISE, and TVu, highlighting its suitability as an excellent solution for dynamic and uncertain conditions in the chemical process industry.