The temporal and spatial distribution of the rainfall, the rainfall-runoff modelling is a nonlinear process. A more thorough understanding of the hydrological system can be attained by combining the usage of HEC-HMS and ANN in rainfall-runoff process simulation. These methods can also improve model performance, quantify uncertainty, compare models, and support operational decision-making. HEC-HMS, with its detailed representation of the physical processes, can provide a strong foundation for understanding the underlying mechanisms driving the rainfall-runoff relationship. ANN, with its ability to learn from data, can complement the HEC-HMS model by capturing the complex, non-linear relationships that may not be fully captured by the physically based model. Deficit and constant loss, Clack unit hydrograph, and Muskingum methods were used for loss, transform and routing respectively in HEC-HMS. The Levenberg-Marquardt (LM) algorithm with three (one input, one hidden, and one output) layers structure using daily rainfall runoff data between 2001 and 2018 was used. The model performance’s has been compared based on the simulation results and both models showed desirable performance. However, HEC-HMS underestimate the peak flow more when compared to ANNs. ANN model results more performance and captured peak flows compared to HEC-HMS. Statistical indicators, including the coefficient of determination (R2), RMSE-observations standard deviation ratio (RSR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS), have been used to assess the effectiveness of each model. NSE, R2, RSR, and PBIAS results 0.811, 0.8256, 0.4 and 8.43% for HE-HMS and 0.93, 0.933, 0.263, and 5.8% for ANN respectively in calibration period (2001-2012). NSE, R2, RSR, and PBIAS results 0.821, 0.8335, 0.4 and 12.59% for HE-HMS and 0.935, 0.936, 0.255, and -0.052% for ANN respectively in validation period (2013-2018). The findings indicate both models show better performance for Gumera catchment. However, ANN show more performance compared to HEC-HMS. Keywords: Artificial Neural Network, Comparison, Gumera Catchment, HEC-HMS, Simulation, Rainfall Runoff