Airline flight route networks exhibit hub-dominated connectivity that causes community detection algorithms like Louvain to overlook regionally significant substructures critical for optimizing operations and improving network resilience. This paper introduces StaRNBRW (Stochastic Reinforcement by Bypassing Random Walks), a complete framework enhancing Louvain community detection tailored for airline flight route networks, where airports are nodes and flight routes are edges. StaRNBRW identifies hubs using betweenness centrality and systematically modifies edge weights via targeted random walks that deliberately bypass hub airports, integrating stochastic processes with graph-based learning to enhance detection of latent community structures and reconfigure networks for operational resilience. The approach substantially penalizes hub-to-hub connections while reinforcing alternative regional routes, resulting in balanced community structures that reflect operational realities rather than mathematical artifacts. When applied to a comprehensive dataset of global flight routes (59,000 routes, 3,425 airports), StaRNBRW significantly increased regional community formation, reducing hub-dominated communities from 100 to 82% while improving network resilience. Most notably, the average shortest path length in the largest connected component decreased by nearly 4%, reflecting improved routing flexibility through alternative pathways and increased route diversity. These results demonstrate both theoretical contributions to community detection methodologies and practical applications for airline contingency planning and traffic management. The approach generalizes to other transportation networks including railways and urban mobility systems, offering a data-driven framework for enhancing infrastructure resilience through strategic decentralization. Complete mathematical formulation and design rationale provided.