Succinic anhydride, a crucial chemical intermediate, finds widespread applications in pharmaceuticals, polymers, and agrochemicals. The carbonylation of acetylene presents a promising route for succinic anhydride synthesis, offering environmental advantages and process efficiency. This study focuses on the empirical modeling of the carbonylation process to optimize the gas ratio of carbon monoxide (CO) to acetylene (C₂H₂), a critical parameter influencing product yield. Regression Analysis was applied to experimental data to develop a predictive model that can be used to predict the yield of succinic anhydride based on the ratio of carbon monoxide (CO) to acetylene (C₂H₂). The linearity connection between the analytical variables was determined by plotting the (CO: C₂H₂) ratio (independent variable) and the yield of the process (dependent variable) against each other. However, the plot's curvilinear form suggested polynomial regression, showing that the connection was not linear. The R² values were 0.9219, 0.9563, and 0.9874 for the data in quadratic, cubic, and quartic models. These findings with statistical tests showed that the quartic model is the best empirical model for explaining the observed data variance (by a margin of 98.74%). The verification of experimental data to validate model acceptability showed an error of less than 0.05. Thus, using this model and changing the ratio of (CO: C₂H₂) from 1.1 to 4.0, the yield of succinic anhydride increased. The insights of this study would contribute to the advancement of sustainable and efficient chemical processes with potential applications in various industries.