Tsunami run-up height is a significant parameter for dimensions of coastal structures.In the present study,tsunami run-up heights are estimated by three different Artificial Neural Network(ANN) models,i.e.Feed Forward Back Propagation(FFBP),Radial Basis Functions(RBF) and Generalized Regression Neural Network(GRNN).As the input for the ANN configuration,the wave height(H) values are employed.It is shown that the tsunami run-up height values are closely approximated with all of the applied ANN methods.The ANN estimations are slightly superior to those of the empirical equation.It can be seen that the ANN applications are especially significant in the absence of adequate number of laboratory experiments.The results also prove that the available experiment data set can be extended with ANN simulations.This may be helpful to decrease the burden of the experimental studies and to supply results for comparisons. Tsunami run-up height is a significant parameter for dimensions of coastal structures. In the present study, tsunami run-up heights are estimated by three different Artificial Neural Network (ANN) models, ie Feed Forward Back Propagation (FFBP), Radial Basis Functions (RBF) and Generalized Regression Neural Network (GRNN). As the input for the ANN configuration, the wave height (H) values ​​are employed. It is shown that the tsunami run-up height values ​​are closely approximated with all of the applied ANN methods. The ANN estimations are slightly superior to those of the empirical equation. It can be seen that the ANN applications are especially significant in the absence of adequate number of laboratory experiments. The results also prove that the available experiment data set can be extended with ANN simulations. This may be helpful to reduce the burden of the experimental studies and to supply results for comparisons.