Location of the peak cylinder pressure and the crank angle associated with half of the energy releases during the combustion process are generally used to define the engine combustion phasing and control the engine efficiency. To accelerate the optimization of a natural gas spark ignition internal combustion engine, this study proposes a black box modeling approach that will reduce the experimental or computational time needed to estimate the high efficient operating conditions at a particular engine speed and load via combustion phasing information. Specifically, a k-nearest neighbors (KNN) algorithm applied key engine operating variables such as the spark timing, air-fuel ratio, and engine speed as inputs to predict combustion phasing parameters such as the crank angles associated with peak cylinder pressure and 50% energy release. After training the correlative model, the selected engine variables produced acceptable errors for most operating conditions investigated. The results showed that the KNN algorithm predicted much better the location of the peak pressure than the location of the 50% energy release, as evidenced by the larger R2 values and smaller prediction errors. In addition, the regression model built in this study produced larger errors in the sparse-distributed region. Therefore, a more uniformly distributed training dataset is suggested for KNN algorithm, at least for the situations investigated in this research.