Converting existing compression ignition engines to spark ignition approach is a promising approach to increase the application of natural gas in the heavy-duty transportation sector. However, the diesel-like environment dramatically affects the engine performance and emissions. As a result, experimental tests are needed to investigate the characteristics of such converted engines. A machine learning model based on bagged decision trees algorithm was established in this study to reduce the experimental cost and identify the operating conditions of special interest for analysis. Preliminary engine tests that changed spark timing, mixture equivalence ratio, and engine speed (three key engine operation variables) but maintained intake and boundary conditions were applied as model input to train such a correlative model. The model output was the indicated mean effective pressure, which is an engine parameter generally used to assist in locating high engine efficiency regions at constant engine speed and fuel/air ratio. After training, the correlative model can provide acceptable prediction performance except few outliers. Subsequently, boosting ensemble learning approach was applied in this study to help improve the model performance. Furthermore, the results showed that the boosted decision trees algorithm better described the combustion process inside the cylinder, as least for the operating conditions investigated in this study.