Te fundamental period is one of the most critical parameters for the seismic design of structures. Tere are several literature approaches for its estimation which ofen conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infll walls into the structure despite the fact that infll walls increase the stiffness and mass of structure leading to signifcant changes in the fundamental period. In the present paper, artifcial neural networks (ANNs) are used to predict the fundamental period of inflled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. Te comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of inflled RC frame structures taking into account the crucial parameters that influence its value.