With the increase in water and energy demands to satisfy industrial processes requirements and convert raw materials into value-added products, natural resources are experiencing depletion stress. One of the effective solutions to decrease freshwater and energy consumption and production in industrial cities is to employ water-energy integration. Due to increasingly strict environmental regulations, integration networks became essential. Water and carbon footprints are reduced significantly via water and energy integration networks. The performance of the integration networks is affected by seasonal changes. Previous work ignored seasonal fluctuations in water/energy supply and demand or mainly utilized multi-period planning to consider seasonal variations while designing integrations networks. It is important to consider seasonal variations to reflect the real performance of the network and avoid operation disturbances. One of the drawbacks of multiperiod planning is the resulting complicated integration model. Multiperiod planning may result in implementation difficulties due to constraints on the piping layout that hinder its applicability. This paper investigates and assesses seasonal changes’ impacts on different segments of the water-energy network using several tools. Based on seasonality assessment, a novel approach is proposed to design optimal water-energy integration network. The approach depends on designing the network units and utility system based on the maximum required capacity (i.e., peak conditions) to ensure that water/energy demands will be satisfied over the year. The water-energy network connectivity is determined based on average demand/supply while any water source-to-sink pipeline is designed based on maximum potential flowrate. The water network is designed based on the worst-case scenario of removal ratios to ensure the required water quality for each sink is satisfied for all connections over different seasons. A MINLP mathematical model was expanded to include the proposed approach. The objective function is to minimize the total annual cost (TAC) of the design. Finally, the framework was demonstrated by applying it to a case study which was solved using a stochastic programming tool to illustrate the applicability of the developed model. The results indicate that the optimal design of the water-energy network that considers seasonal changes in water/energy demands, and supplies can be achieved with the proposed method with a TAC of 78 MUSD/y without the need for multiperiod planning. The optimal treatment units selected in this case were one-stage and two-stage nanofiltration.