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In this study, the artificial neural network (ANN) was used to model ferromagnetic Ising hysteresis obtained from mean-field analysis as a case study. ANNs were trained to predict the effect of external perturbations, which are the temperature, the field amplitude and the field frequency, on the hysteresis properties, which are the hysteresis area, the remanence magnetization and the coercivity. The input data to the ANN were split into training data, testing data and validating data. Search were carried out to identify number of hidden layer and number of hidden nodes to find the best architecture with highest accuracy. After the networks had been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the actual data were found to match very well over an extensive range. This therefore suggests a success in modeling ferromagnetic hysteresis properties using the ANN technique.