<p>Yager’s measure-based granular uncertainty (MBGU) method, utilizing the Choquet integral and representative payoffs, is effective for decision-making under uncertainty. However, it faces limitations when extended to the Pythagorean fuzzy environment due to its higher-order uncertainty representation. To address this gap, we propose a novel decision-making framework that integrates MBGU with Pythagorean fuzzy sets (PFSs), enhancing its capability to handle complex and ambiguous scenarios. The proposed approach generalizes MBGU to the Pythagorean fuzzy domain, ensuring a more robust handling of indeterminate information. Numerical examples and a real-world application validate the framework’s effectiveness, demonstrating its superiority over traditional MBGU methods in representing intricate decision problems and improving decision accuracy. This study offers a significant advancement in intelligent decision-making under granular and fuzzy uncertainty.</p>