Rough set theory provides a mathematical framework for approximating subsets through lower and upper bounds defined by equivalence relations, effectively capturing uncertainty in classification and data analysis. Building on these foundational ideas, extended models such as Hyperrough Sets and Superhyperrough Sets have been proposed to represent more complex forms of uncertainty. In this paper, we introduce the HyperRough TOPSIS Method and the SuperHyperRough TOPSIS Method, and examine their underlying mathematical structures. TOPSIS is a well-established method in decision-making, and the proposed HyperRough and SuperHyperRough TOPSIS methods serve as generalized extensions of the classical Rough TOPSIS approach.