The prerequisite in the majority of control processes is modeling. The model used to design a controller must be both accurate and real-time. Utilizing prevalent approaches of modeling, namely modeling based on (numerically) solving the equations governing the fluid in the combustion chamber, is too time-consuming and not suitable for a control purpose. This paper is to model combustion in an SI engine by means of neural networks and present an accurate and fast-response model for combustion. Obviously, any training procedure of neural networks does involve empirical data acquisition. On the other hand, engine testing is highly expensive, and testing data tables available (in industry) are not sufficient to train neural networks. In this paper, first with the aid of a CFD software, a one-dimensional model of an engine is constructed, and then calibrated using factual experimental data at hand. Afterwards, acquiring data required is performed via the validated CFD model. As a matter of fact, because of not having access to necessary experimental coefficients, calibration is an extremely complicated and time-consuming process. It will be attempted to accomplish and spell out the calibration of the engine model in the GT-Power software, in a scientific practice. After a brief survey on the methods employed in designing the neural networks, modeling of the combustion chamber will be stated. Eventually, the response of the constructed NN model will be compared to the results gained from the GT-Power software, and the great accuracy of the NN model will be indicated.