In the literature, predictions for the occurrence of nonlinear soil liquefaction in soil deposits have been investigated through numerous empirical methods. These methods which are also known as 'conventional techniques' were derived from several in-situ tests, laboratory tests and case records. An alternative general regression neural network (GRNN) model that addresses the collective knowledge built in a simplified procedure is proposed. To meet this objective, a total of 3895 case records including twelve soil and seismic parameters driven mostly from the cone penetration test results are introduced into the model. The data includes the results of field tests from the two major earthquakes that took place in Turkey and Taiwan in 1999 and some of the desired input parameters are obtained from correlations existing in the literature. The soil liquefaction decision in terms of seismic demand and capacity is determined by a recognized simplified approach, namely a stress-based method and a strain-based method. Furthermore, the liquefaction probability of soils with significant fines is tested with the so-called Chinese Criteria. The proposed GRNN model is developed in four phases, mainly: the identification phase, collection phase, implementation phase, and verification phase. An iterative procedure followed to maximize the accuracy of the proposed model. The case records were divided randomly into testing, training, and validation datasets. The proposed GRNN model effectively explored the complex relationship between the introduced soil and seismic input parameters and validated the liquefaction decision.