<p>This research presents a novel optimization method, called Fuzzy Generalized Type 2 Multiverse Optimizer Algorithm (FMVOgt2), derived from the fusion of the Multiverse Optimizer with type 2 fuzzy logic. The method is designed to enhance convergence behavior and adaptive parameter control during the optimization process. We evaluate the method on 13 standard benchmark functions in 5, 50, and 100 dimensions with 30 independent runs and 500 iterations, and we assess significance with a 95% one-tailed <i>Z</i> test. The strongest gains appear in high-dimensional settings and are corroborated by two control problems, an inverted pendulum and shower temperature regulation, where generalized type 2 tuning produces smoother parameter evolution and more robust closed-loop behavior; the generalized type 2 inference is executed offline during tuning, so deployed controllers have no run-time overhead. Comparative experiments are conducted against original variants from earlier studies, including standard MVO and fuzzy-chaotic extensions. The objective is to demonstrate how the incorporation of type-2 fuzzy adaptation within the multiverse search framework can offer improved precision and robustness in both optimization and real-time fuzzy control design.</p>