Accurate river flow prediction is critical for sustainable water resource management, particularly in arid and semi-arid regions. However, balancing model accuracy, computational efficiency, and interpretability remains a significant challenge due to the complex and nonlinear nature of hydrological systems. This study employs a granular computing (GRC) model to predict monthly inflows to the Alavian Dam in Iran. Principal component analysis (PCA) was used to reduce input dimensionality, identifying six key variables to enhance computational performance. The predictive performance of GRC was compared with artificial neural networks (ANNs) and support vector machines (SVMs), using R2, RMSE, and MAE as evaluation metrics. The GRC model achieved R2 values of 0.93 during calibration and 0.94 during validation, outperforming both ANN and SVM. Notably, GRC demonstrated superior accuracy in capturing extreme flow events, which are crucial for flood and drought management. This advantage is attributed to its rule-based structure and local learning approach, which enables effective modeling of nonlinearities and sparse data. Furthermore, the interpretability of the GRC model - facilitated by its use of granules and transparent if-then rules - offers valuable insights into variable influence. These strengths highlight GRC as a reliable and efficient tool for hydrological forecasting and climate-adaptive water resource planning.