This study aimed to mitigate environmental risks in energy production through the design of a system that generates high-quality syngas from a blend of poplar wood and polyethylene terephthalate waste. CO2 was employed as the gasifying agent, an approach that both eliminates nitrogen dilution in the syngas stream and offers a practical pathway for CO2 utilization from industrial emissions, thereby linking clean energy production with greenhouse gas reduction. To assess the validity and robustness of the developed models, a residual analysis was performed. Subsequently, a bi-objective optimization was conducted to simultaneously maximize cold gas efficiency and the H2/CO ratio. The reliability of the machine learning model was evaluated by comparing its predictions with the outcomes derived from thermodynamic simulations. The results demonstrated that the optimal operating range was within a gasifier agent to fuel of 1.95–2.15 and a water gas shift reactor agent to fuel of 1.75–1.90. In this range, the system achieved cold gas efficiencies between 97% and 98%, along with H2/CO ratio percentage ranging from 80% to 90%. The comparative analysis indicated that the results predicted by machine learning models showed strong agreement with those obtained from the engineering equation solver simulation software.
