Machine learning (ML) is increasingly used to predict heavy metal (HM) interactions with biochar, offering insights into adsorption mechanisms and performance. Biochar, a carbon-rich material, is effective for HM removal due to its high surface area, functional groups, and stability. However, challenges remain in data collection, algorithm development, and model interpretability. ML models, such as support vector machines (SVMs), neural networks, and random forests, are used to predict HM adsorption capacity, with factors like pH, initial concentration, and surface area influencing performance. Interpretability methods, including partial dependence plots (PDP), local surrogate (LIME), and Shapley values (SHAP), help understand model predictions and underlying mechanisms. Despite progress, data scarcity and model bias remain issues, requiring more comprehensive datasets and advanced techniques. Future research should focus on improving data quality, expanding descriptor sets, and enhancing model interpretability. ML applications in biochar for HM removal are promising but require further development to address current limitations and improve accuracy and reliability. The integration of ML with experimental data and advanced algorithms is essential for optimizing biochar performance and advancing environmental applications.Machine learning (ML) is increasingly used to predict heavy metal (HM) interactions with biochar, offering insights into adsorption mechanisms and performance. Biochar, a carbon-rich material, is effective for HM removal due to its high surface area, functional groups, and stability. However, challenges remain in data collection, algorithm development, and model interpretability. ML models, such as support vector machines (SVMs), neural networks, and random forests, are used to predict HM adsorption capacity, with factors like pH, initial concentration, and surface area influencing performance. Interpretability methods, including partial dependence plots (PDP), local surrogate (LIME), and Shapley values (SHAP), help understand model predictions and underlying mechanisms. Despite progress, data scarcity and model bias remain issues, requiring more comprehensive datasets and advanced techniques. Future research should focus on improving data quality, expanding descriptor sets, and enhancing model interpretability. ML applications in biochar for HM removal are promising but require further development to address current limitations and improve accuracy and reliability. The integration of ML with experimental data and advanced algorithms is essential for optimizing biochar performance and advancing environmental applications.