The design and optimization of rain gauge networks are pivotal for accurate hydrologic modelling and effective flood management. This study explores the application of the Analytic Hierarchy Process (AHP) to rank and select key rain gauges, forming an optimized network that enhances hydrologic model performance. Initially, rain gauges were ranked using AHP based on various criteria, including their importance in capturing spatial rainfall variability and their statistical relationship with the areal average rainfall. The selection of key rain gauges was guided by statistical analyses that identified stations significantly contributing to the overall rainfall pattern representation. The designed network, comprising these key rain gauges, was subsequently integrated into a hydrologic model to simulate discharge. The performance of the hydrologic model was evaluated by comparing observed and simulated discharge data. Performance metrics, including the correlation coefficient, Nash–Sutcliffe Efficiency (NSE), Normalized Root Mean Square Error (NRMSE), and Index of Agreement (IOA), were computed to assess the model's accuracy and reliability. Results indicated that the AHP-based selection method significantly improved the efficiency and accuracy of the hydrologic model. Model-AHP exhibits higher NSE values of 0.727 and IOA values of 0.904 compared to the model with all twenty-six rain gauges, which have NSE of 0.563 and IOA of 0.821. This difference is attributed to the overlapping representative areas for each rain gauge station in the basin and the inadequate spatial distribution in the latter model. This optimized network is particularly beneficial in scenarios requiring rapid runoff predictions, such as flash floods, where time-efficient modelling is crucial for effective emergency response. The study demonstrates that AHP is a powerful tool for designing rain gauge networks, enabling more efficient and accurate hydrologic modelling. The key rain gauge network developed through this approach can be effectively used in flood management and emergency scenarios, providing timely and reliable runoff predictions.