The gasification of residues into syngas offers a versatile gaseous fuel that can be used to produce heat and power in various applications. However, the application of syngas in engines presents several challenges due to the changes in its composition. Such variations can significantly alter the optimal operational conditions of the engines that are fueled with syngas, resulting in combustion instability, high engine variability, and misfires. In this context, this work presents an experimental investigation conducted on a port-fuel injection spark-ignition optical research engine using three different syngas mixtures, with a particular focus on the effects of CO/H2 and diluent ratios. A comparative analysis is made against methane, considered as the baseline fuel. The in-cylinder pressure and related parameters are examined as indicators of combustion behavior. Additionally, 2D cycle-resolved digital visualization is employed to trace flame front propagation. Custom image processing techniques are applied to estimate flame speed, displacement, and morphological parameters. The engine runs at a constant speed (900 rpm) and with full throttle like stationary engine applications. The excess air–fuel ratios vary from 1.0 to 1.4 by adjusting the injection time and the spark timing according to the maximum brake torque of the baseline fuel. A thermodynamic analysis revealed notable trends in in-cylinder pressure traces, indicative of differences in combustion evolution and peak pressures among the syngas mixtures and methane. Moreover, the study quantified parameters such as the mass fraction burned, combustion stability (COVIMEP), and fuel conversion efficiency. The analysis provided insights into flame morphology, propagation speed, and distortion under varying conditions, shedding light on the influence of fuel composition and air dilution. Overall, the results contribute to advancing the understanding of syngas combustion behavior in SI engines and hold implications for optimizing engine performance and developing numerical models.