Objectives: The Pythagorean fuzzy shortest hyperpath problem balances distance minimization with network fuzziness and uncertainty, considering node lengths, membership and non-membership degrees, when accurate data is unavailable or traditional methods cannot handle uncertain information. Methods: Pythagorean Fuzzy Dijkstra’s Algorithm uses Pythagorean fuzzy numbers as edge weights, while the A* algorithm considers fuzziness and uncertainty. Evolutionary, metaheuristic, linear programming, and fuzzy decision-making techniques can be used to solve the Pythagorean fuzzy shortest hyperpath problem. Findings: The Pythagorean fuzzy shortest hyper path algorithm is a useful tool for navigating complex networks under uncertainty, identifying optimal routes, quantifying uncertainty, and providing sensitivity, trade-off, and network resilience analysis. It’s applicable in transportation, logistics, telecommunications, and urban planning. Novelty: The Pythagorean fuzzy shortest hyper path algorithm is a network pathfinding method that uses fuzzy logic principles in graph theory. It quantifies ambiguity along paths, enabling informed decision-making in uncertain environments. This algorithm is applicable across various domains, bridging theoretical concepts with practical applications.