Soil liquefaction is a phenomenon that occurs due to the loss of resistance of saturated soil mass during an earthquake. In this case, the structure built on this soil loses its static equilibrium and overturns. Therefore, predicting liquefaction is a very important issue in civil engineering. There are various methods for determining the potential of soil liquefaction, most of which are based on field tests and empirical equations, which can sometimes be accompanied by a large approximation. Today, the use of modern artificial intelligence methods is of great importance for solving complex engineering problems. Accordingly, in this paper, an attempt was made to predict the occurrence of liquefaction in a region by using an artificial neural network and optimizing it with the Taguchi design of experiments method. In the first step, by collecting existing data on the occurrence of liquefaction in different parts of the world, two datasets were created based on CPT and SPT tests. Next, using the Taguchi design of experiments method, the components of the artificial neural network, including the number of neurons in the middle layers and the activation function, were optimized, and the highest accuracy was obtained for the two datasets of CPT and SPT, equal to 0.89 and 0.73, respectively. Then, the evaluation of the obtained results with the results of 5 different machine learning models, including support vector machine, logistic fitting, k-nearest neighbor, decision tree, and random forest, showed that the optimized artificial neural network gives the best results. Finally, comparing the results of the present model with the experimental method of calculating the liquefaction coefficient shows the higher accuracy of the present model.