###### Abstract  
Predicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships and variability. Machine learning has emerged as a powerful tool for optimizing and managing these processes. This study uses Bayesian optimization to tune parameters for various machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Elastic Net, Adaptive Boosting (AdaBoost), Gradient-Boosting Regressor (GBR), K-nearest Neighbors (KNN), and Decision Tree (DT), aiming to identify the best model for predicting the compositions of CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\) under different conditions. Performance was evaluated using the correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Relative Absolute Error (RAE), and execution time, with comparisons visualized using a Taylor diagram. Hyperparameter optimization's significance was assessed via \(t\)-test effect size and Cohen's d. XGBoost outperformed other models, achieving high R values under optimal conditions (0.951 for CO, 0.954 for CO\({}_{2}\), 0.981 for H\({}_{2}\), and 0.933 for CH\({}_{4}\)) and maintaining robust performance under suboptimal conditions (0.889 for CO, 0.858 for CO\({}_{2}\), 0.941 for H\({}_{2}\), and 0.856 for CH\({}_{4}\)). In contrast, K-nearest Neighbors (KNN) and Elastic Net showed the poorest performance and stability. This study underscores the importance of hyperparameter optimization in enhancing model performance and demonstrates XGBoost's superior accuracy and robustness, providing a valuable framework for applying machine learning to energy management and environmental monitoring. b 2024 2025 15 1018 2025 1018 2025 1018 2025 15  
Recognized as an environmentally friendly solution, biomass energy effectively utilizes organic waste from agriculture, forestry, and other sources. This renewable energy option mitigates waste management challenges and significantly reduces pollution and greenhouse gas emissions [3]. It can be harnessed through various thermochemical and biochemical processes to produce electricity, heat, and biofuels. One such method is biomass gasification, which converts organic materials into energy-rich gases through oxidation. The resulting gases, such as CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\), have high energy content and can be utilized for energy generation, while having significant environmental implications. However, the quantities and compositions of these gases directly impact the efficiency and environmental footprint of the gasification process [4]. Accurate prediction of gas compositions in biomass gasification is thus essential for optimizing process efficiency and minimizing environmental impacts. The utilization of biomass energy is promoted globally as a means of achieving sustainable development goals. ASEAN countries regard biomass energy as a significant rural development and security resource, targeting 10% of their total energy mix to be obtained from modern biomass energy by 2030 [5]. This strategy aims to deliver economic and environmental benefits, such as reducing greenhouse gas emissions, increasing rural employment, and expanding the adoption of renewable energy. Similarly, G7 countries consider biomass energy a low-carbon alternative and invest in biomass projects to reduce carbon emissions. Countries like Canada, Germany, and Japan contribute to sustainable development by investing in innovative biomass-based technologies. These policies aim to accelerate the integration of biomass into energy portfolios, fostering a more sustainable energy future [6]. Predicting gas compositions is critical to improving process efficiency and environmental sustainability in biomass gasification processes. This complexity arises due to the gasification process's nonlinear and dynamic nature, which traditional models often struggle to capture.