Nowadays, the increasing occurrence of natural disasters and human crises such as floods, earthquakes, and pandemics has posed significant challenges to humanitarian supply chains in terms of demand forecasting, infrastructure destruction, and disruption of relief routes, doubling the critical importance of designing networks with rapid response capabilities and high stability. This study addresses the design of a multi-echelon humanitarian supply chain network formulated to simultaneously minimize the total network costs and enhance resilience by reducing penalties associated with critical nodes and vulnerable points. A hybrid fuzzy-stochastic robust optimization approach is employed to handle uncertainties inherent in demand and cost parameters. To convert the multi-objective model into a single-objective formulation, the Lp-metric method is applied. Given the computational complexity of the problem, metaheuristic algorithms—Enhanced Particle Swarm Optimization (EPSO), Traditional Particle Swarm Optimization (TPSO), Genetic Algorithm (GA), and Simulated Annealing (SA)—are developed to solve large-scale instances. The results of implementing the model in a case study of the Golestan province flood and subsequent sensitivity analyses indicate that increased demand and disruption severity directly elevate critical node penalties and total network costs; however, the proposed model guarantees network capability in maintaining service levels and improving relief agility under critical scenarios through the establishment of surplus capacity and the utilization of backup suppliers.