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In the foreseeable future, the growing energy crisis and environmental pollution problem pose severe challenges to the automobile powertrains and exhaust systems. However, conventional optimization methods, including multi-dimensional computational fluid dynamics model and bench experiments, are very time-consuming or expensive. Adding the application of data-driven models to engine research and development has the potential to reduce computational costs or the number of in-depth experiments. This purpose of this study was to compare the performance of widely used artificial neural network (ANN) and random forest (RF) model for predicting the fuel consumption and engine-out emissions of a calibrated spark ignition (SI) engine for any given condition.