The experimentation in internal combustion engines is tedious task owing to its complexity in various aspects. The task becomes more difficult when an unconventional fuel is tested in the conventional engine, e.g., biogas as major fuel in spark ignition (SI) engines. Therefore, for the present study, a data-driven approach has been adopted using artificial neural network modeling for the prediction of combustion parameters of a biogas fueled SI engine. In fact, there have been many research investigations that encourage the use of biogas as a major fossil-based fuel substitute. The majority of the literature is restricted to the testing of biogas in compressed ignition engines, following the dual fuel strategy. The proposition of using biogas in spark ignition engines has not been widely adopted due to certain drawbacks like reduction in engine power output, inferior fuel conversion rate, and inferior emissions associated with the engine. Additionally, at normal pressure and temperature, biogas demands a distinct approach to the fuel induction mechanism. In order to employ biogas as an effective and efficient fuel source in spark ignition engines, fuel induction system has been used and being implemented. The ANN system is trained with experimental data such as speed, compression ratio, and crank angle. The in-cylinder pressure, net heat release rate (NHRR), cumulative heat release rate (CHRR), mass fraction burnt (MFB), and mean gas temperature (MGT) are the predicted outputs. The predicted results found having a close match with the experimental results.