Homogeneous mixture compression ignition (HCCI) internal combustion engines are considered to be a potentially new kind of combustion due to the combustion mechanism that occurs inside these engines. This is because it improves the efficiency of the engine and efficiently lowers the levels of nitrogen oxides. Furthermore, the improvement and control of internal combustion engines, which are amazingly homogeneous mixture compression ignition engines, have always been something that has captured my interest. The cylinder pressure is used as a form of feedback by controllers for internal combustion engines. A costly method that requires the use of dependable computer systems is required to get an exact measurement of the pressure inside the cylinder via the utilization of pressure sensors. This research has developed a model that uses a dynamic multilayer perceptron neural network to determine the ideal average pressure for a homogeneous mixture compression ignition engine. A dynamic model of a homogeneous mixture compression ignition engine was utilized using the MATLAB program in order to get the required data for neural network training. This was done prior to the training of the neural network.