Predicting internal combustion (IC) engine variables such as the combustion phasing and duration are essential to zero-dimensional (0D) single-zone engine simulations (e.g., for the Wiebe function combustion model). This paper investigated the use of random forest machine learning models to predict these engine combustion parameters as a modality to reduce expensive engine dynamometer tests. A single-cylinder four-stroke heavy-duty spark-ignition engine fueled with methane was operated at different engine speeds and loads to provide the data for training, validation, and testing the proposed correlated model. Key engine operating variables such as spark timing, mixture equivalence ratio, and engine speed were the model inputs. The performance of the models was validated by comparing the prediction dataset with the experimentally measured results. Results showed that the prediction error of the random forest machine learning algorithm was acceptable, suggesting that it can be used to predict the combustion parameters of interest with acceptable accuracy.