A prominent parameter in dealing with swash and morphological evolution is the runup length or height, defined as the limit of landward sea. Therefore, it is necessary to predict the runup height in this area. In this paper, the abilities of a new Adaptive-Network-Based Fuzzy Inference System (ANFIS) using subtractive fuzzy clustering method, Fuzzy Inference System (FIS), and existing empirical formulas are implemented for predicting and modeling wave runup in the swash zone. The ANFIS and FIS models are established using the slope angle; Iribarren number and antecedent wave runup data. The empirical formulas are also applied to the same data. Statistical measures were used to evaluate the performance of the models. The existing wave runup, bottom slope, and deep water Iribarren number data for regular and irregular waves on smooth, impermeable plane slopes were used as case studies. The comparison of results reveals that, the ANFIS model provides high accuracy and reliability for wave runup estimation, providing better predictions compared to other techniques. The paper demonstrates that the neurofuzzy approach developed is a good trade-off between the advantages of the Neural Network (supervised learning capabilities) and of the Fuzzy Logic (knowledge which can be explained and understood) without the generic drawbacks (overfitting, optimization problems, etc.) typical in other approaches.