In first stage, a machine learning (ML) was performed to predict in-cylinder pressure using both fuzzy logic (FL) and artificial neural networks (ANN) depending on the results of experimental studies in a spark ignition (SI) engine. In the ML phase, the experimental in-cylinder pressure data of SI engine was used. SI engine was operated at stoichiometric air–fuel mixture (φ = 1.0) at 1200, 1400, and 1600 rpm engine speeds. Six different ignition timings, ranging from 15 to 45 °CA, were used for each engine speed. Correlations (R2) between data from in-cylinder pressure obtained via FL and ANN models and data form experimental in-cylinder pressure were determined. R2 values over 0.995 were obtained at an ML stage of ANN model for all test conditions of the engine. However, R2 values were remained between range of 0.820–0.949 with the FL model for different engine speeds and ignition timings. In the second stage, in-cylinder pressure prediction was performed by using an ANN model for engine operating conditions where no experimental results were obtained. Furthermore, indicated mean effective pressure (IMEP) values were calculated by predicting in-cylinder pressure data for different engine operation conditions, and then compared with experimental IMEP values. The results show that the in-cylinder pressure and IMEP results estimated with the trained ANN model are fairly close to the experimental results. Moreover, it was found that using the trained ANN model, the ignition timing corresponding to the maximum brake torque (MBT) used in the engine management systems and engine studies could be determined with high accuracy.