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This paper uses Multivariate Adaptive Regression Spline (MARS) and Least Squares Support Vector Machines (LSSVMs) to predict hysteretic energy demand in steel moment resisting frames. These models are used to establish a relation between the hysteretic energy demand and several effective parameters such as earthquake intensity, number of stories, soil type, period, strength index, and the energy imparted to the structure. A total of 27 datasets (input–output pairs) are used, 23 of which are used to train the model and 4 are used to test the models. The data-sets used in this study are derived from experimental results. The performance and validity of the model are further tested on different steel moment resisting structures. The developed models have been compared with Genetic-based simulated annealing method (GSA) and accurate results portray the strong potential of MARS and LSSVM as reliable tools to predict the hysteretic energy demand.