One of the most important issues in designing coastal and offshore structures is the prediction of wave and current forces on slender cylinders. Such forces are often considered as dominate loadings. Many analytical and empirical methods such as Morison equation have been suggested for estimation of waves and current forces. Such methods, however, have shown inaccuracies in predicting hydrodynamic forces. On the other hand, Artificial Neural Networks (ANNs) have received a great deal of attention in recent years and are being touted as one of the greatest computational tools ever developed. In fact, ANNs are nonlinear systems consisting of a large number of highly interconnected processing units, nodes or artificial neurons, which have the ability of learning. In this research, ANNs have been used to estimate wave and current forces on slender cylinders. Data of 308 experimental specimens have been used for training and testing the networks. Considering the aim of this research for the application of ANNs, these data were consisted of recorded force values in different time series. The supervised learning neural networks models have been used in this research. The results indicate the success of the application of neural networks approach which can efficiently predict waves and current forces on slender cylinders after carrying out appropriate training. Furthermore, the results are within acceptable accuracy in comparison with experimental results and the results obtained from Morison equation.