Gasification is a complex process characterized by nonlinear physicochemical interactions and variable operating conditions, rendering conventional modelling tools, which rely on simplistic assumptions, inadequate for precise predictions. This study addresses these issues by developing and accessing three machine learning models—Linear Regression, Decision Tree, and XGBoost—to forecast the syngas yield from the co-gasification of coal and biomass. The parity plots, residual density analysis, and fundamental statistical metrics were employed to assess performance. The Linear Regression model achieved training and testing R2 values of 0.8719 and 0.8119, respectively. Nonetheless, it exhibited a substantial testing mean absolute percentage error (MAPE) of 127.90%, indicating difficulty in capturing nonlinearities. The Decision Tree model significantly improved performance, with a training R2 of 0.9890, a testing R2 of 0.8912, and a testing MAPE that was reduced by 67.23%. The XGBoost model demonstrated optimal performance, with a testing R2 of 0.9844 and a testing MAPE of 64.09%, indicating a nearly perfect match. The results indicate that advanced machine learning techniques, particularly XGBoost, enhance the reliability and accuracy of gasification process modelling, hence facilitating data-driven process optimization.