The article discusses the application of machine learning (ML) in predicting heavy metal (HM) interactions with biochar, a carbon-rich material produced through pyrolysis of biomass. Biochar is an effective adsorbent for removing HMs from water and soil, but its performance is influenced by various factors such as biochar properties, experimental conditions, and HM characteristics. The authors highlight the challenges in data collection, algorithm development, and data representation, which impact the accuracy and generalizability of ML models. They emphasize the importance of selecting appropriate descriptors, such as biochar pH, surface area, and functional groups, to improve model performance. The article also reviews recent studies that use ML to predict biochar adsorption capacity, interpret underlying mechanisms, and design new materials. Techniques like sensitivity analysis, partial dependence plots, and Shapley values are discussed for enhancing model interpretability. Additionally, the authors propose data augmentation methods, such as Gaussian noise-based augmentation, to improve model generalization. The article concludes by discussing future perspectives and challenges, including the need for more comprehensive datasets, improved model interpretability, and the integration of advanced machine learning techniques to enhance the feasibility and reliability of biochar-based environmental remediation.The article discusses the application of machine learning (ML) in predicting heavy metal (HM) interactions with biochar, a carbon-rich material produced through pyrolysis of biomass. Biochar is an effective adsorbent for removing HMs from water and soil, but its performance is influenced by various factors such as biochar properties, experimental conditions, and HM characteristics. The authors highlight the challenges in data collection, algorithm development, and data representation, which impact the accuracy and generalizability of ML models. They emphasize the importance of selecting appropriate descriptors, such as biochar pH, surface area, and functional groups, to improve model performance. The article also reviews recent studies that use ML to predict biochar adsorption capacity, interpret underlying mechanisms, and design new materials. Techniques like sensitivity analysis, partial dependence plots, and Shapley values are discussed for enhancing model interpretability. Additionally, the authors propose data augmentation methods, such as Gaussian noise-based augmentation, to improve model generalization. The article concludes by discussing future perspectives and challenges, including the need for more comprehensive datasets, improved model interpretability, and the integration of advanced machine learning techniques to enhance the feasibility and reliability of biochar-based environmental remediation.