<p>Runoff modeling is essential for effective water resource management and flood forecasting, offering insights into the complex interactions between precipitation, land surface characteristics, and resultant runoff. Among various hydrological models, the Soil Conservation Service Curve Number (SCS-CN) model stands out for its simplicity and wide applicability in estimating direct runoff, particularly in small to medium watersheds. This review examines the evolution of the SCS-CN model, its integration with Geographic Information Systems (GIS), and recent advancements, including artificial neural networks (ANNs) as a complementary approach. While the SCS-CN model is valued for its empirical robustness and ease of use, its limitations, such as assumptions of uniform rainfall and spatial homogeneity, necessitate enhancements. This paper highlights region-specific calibration, flexible initial abstraction ratios, and dynamic CN adjustments based on real-time data as potential improvements. The comparative analysis with ANN demonstrates the latter superior ability to capture non-linear hydrological processes, albeit with higher computational demands. The study underscores the importance of selecting context-appropriate modeling strategies to enhance runoff predictions, mitigate flood risks, and inform sustainable water resource management practices under changing climatic conditions.</p>