Plasma catalysis is capable of significantly enhancing the energy conversion efficiency of the ${\mathrm{CO}}_2$ reforming of methane. Simulation is an effective method for studying internal principles and operational mechanisms of the plasma-catalyzed ${\mathrm{CO}}_2$ reforming of methane. However, simulation has some potential problems such as poor convergence and high computational complexity. To address these challenges, a stacking ensemble learning-assisted simulation of the plasma-catalyzed ${\mathrm{CO}}_2$ reforming of methane was proposed. The stacking ensemble model, trained on limited converged simulation data, interpolates non-convergent points by leveraging the combined predictive power of multiple base models (KNN, DT, XGBoost). This approach ensures that predictions remain within the training data’s parameter space, minimizing extrapolation risks. We utilize Bayesian optimization and stacking ensemble methods aimed at improving the accuracy and generalization capability of this model. Experimental results show that this model can provide accurate CO density values under different $E/N$ and ${\mathrm{CO}}_2$ gas-feeding ratio conditions. The comparative analysis results also demonstrate that Bayesian optimization and ensemble techniques can effectively improve model accuracy. This model combines advanced machine learning techniques with traditional simulation techniques. The time for predicting particle density under new experimental conditions has been reduced from 24 min in numerical simulation to a few seconds, which is 99.8% less than traditional 0D simulations, while maintaining high prediction accuracy (R² = 0.9795).