Abstract: Prototype selection has become a large area of current research in statistical learning. We introduce a novel topological data analysis (TDA)-based framework for selecting representative prototypes from large datasets.  We show that this approach preserves classification performance while substantially reducing data size. Such methods are crucial in resource-constrained environments where memory and computation are limited. Together, these contributions advance both algorithmic and geometric aspects of prototype learning and offer practical tools for scalable, interpretable, and potentially efficient classification.