This study presents a computational framework that integrates an air-standard thermodynamic engine model with artificial neural networks to predict the performance of spark-ignition (SI) engines operating with alternative fuels of reduced lower heating value (LHV). A deterministic thermodynamic simulator was developed in Excel, incorporating engine geometric parameters (compression ratio, bore, stroke, displacement), operating speed, and fuel properties, with particular emphasis on LHV as the dominant energetic descriptor. The model computes in-cylinder states, thermal efficiency, indicated mean effective pressure, and power output under idealized air-standard assumptions. To extend predictive capability beyond fixed-parameter analyses, a feedforward neural network was trained using datasets generated from systematic parametric sweeps of engine geometry, speed, and fuel LHV. The neural network captures nonlinear interactions between compression ratio, combustion energy release, and