Inelastic displacement demand is an important part of the performance-based design and it should be estimated realistically to determine a reliable seismic performance of a structure. In this context, the coefficient method is an easy and practical method for this estimation. The coefficient method is a method that is used to estimate inelastic displacement demand by the multiplication of the elastic displacement demand and inelastic displacement ratio. Thus, it is clear that a reliable estimation of inelastic displacement demand depends on a reliable inelastic displacement ratio. After a reliable estimation of the inelastic displacement ratio, it is essential to propose an equation for the usage of engineering practice. Although nonlinear regression analysis is preferred in the literature as a classical method to estimate an equation, the Artificial Neural Network method is a new and modern way that can be used in the esti-mation of inelastic displacement ratio. In this study, Artificial Neural Network models have been proposed by using data of inelastic displacement ratios of Single Degree of Freedom systems with stiffness and strength degrading peak-oriented hysteretic model and collapse potential by performing nonlinear time history analyses. Firstly, a large number of trials have been conducted to obtain an optimum Artificial Neural Network model. The results of Ar-tificial Neural Network models have been compared to the results of equation estimated by using nonlinear regression analysis and given in the previous studies. According to the results, Artificial Neural Network models give closer values to the inelastic displacement ratios of time history analysis than nonlinear regression analysis. Especially, the Bayesian Regulariza-tion Backpropagation model of the Artificial Neural Network method with two hidden layers achieved the best performance among the other Artificial Neural Network models. It can be said that Artificial Neural Network methods can be used to estimate inelastic displacement ratio since it yields better accuracy than previous techniques for different parameters.