For the purpose of creating reliable prediction models for breast cancer screening, precise and effective feature selection is critical. The multiverse optimization (MVO) algorithm is employed in this paper to demonstrate a unique feature selection approach for handling breast cancer datasets. The MVO algorithm is inspired by the notion of parallel universes and how they interact to discover optimal feature subsets by exploring a variety of solution spaces. In an effort to avoid local optima and achieve more powerful feature selection, MVO balances exploration and exploitation by simulating several universes, each governed by unique rules of physics. Our test findings on popular breast cancer datasets reveal that, in comparison with conventional techniques, the proposed MVO-based feature selection significantly boosts classification accuracy and decreases computing complexity. This work establishes a new standard for feature selection methods in machine learning and emphasizes the potential of multiverse-inspired algorithms for enhancement of medical diagnostics.