<p>In today’s commercial world, drones are emerging as a modern, growing method for urban delivery, providing significant competitive advantages. Despite its substantial economic and environmental advantages, which contribute to the sustainability of delivery networks, there are operational limitations regarding the effective utilization of drones that make it challenging to design delivery networks. Integrated decisions on warehouse and charging-station locations, as well as allocation and charging scheduling under the impact of operational uncertainties, are particularly important. The energy consumption of drones plays a key role in the feasibility of trips and network decisions due to the influence of environmental conditions. In this study, an integrated location-allocation-charging model for drone delivery networks is proposed, aiming to reduce costs and increase network robustness in the presence of uncertainties related to demand and drone energy consumption. In this model, drone energy consumption is modeled using a fuzzy-robust approach. Specifically, uncertainty due to environmental factors is introduced into the model through fuzzy trapezoidal numbers, the Me criterion, and a conservatism parameter <i>k</i>, to simultaneously cover both the effect of environmental factors and their extreme fluctuations, without disrupting the linear structure of the model. Additionally, to increase network robustness in the presence of demand fluctuations, demand uncertainty is modeled using the Bertsimas-Sim budget robustness approach. The simultaneous presence of fuzzy-robust energy consumption uncertainty and robust demand uncertainty in an integrated location-allocation-charging framework addresses the literature gap and provides an operational, flexible, and robust model for designing drone delivery networks. To investigate the behavior of the model, several numerical examples and sensitivity analyses were performed with respect to various parameters, including the uncertainty budget parameter, backup capacity, unmet demand penalty, and environmental coefficient. Furthermore, a case study is also provided to test the operational model in real-world conditions. Results indicate that the proposed model is robust in the presence of demand uncertainties and drone energy consumption and fluctuations in various parameters, while optimizing network costs and maintaining its efficiency for application in real-world environments.</p>