The contamination of water and soils with heavy metals poses a significant environmental threat, making the development of effective removal strategies a global priority. Hence, the determination of heavy metals can play an essential role in environmental monitoring and assessment. In the current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient Boosting (GB), HistGradientBoosting, Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM)) were applied in attempt to predict the adsorption efficiency of several heavy metals (i.e., Pb, Cd, Ni, Cu, and Zn) according to different factors including temperature, pH, and biochar characteristics. Data were collected from open-source literature review including 353 samples. At the first stage, data processing was performed including outliers' removal and scaling for better data modeling applicability; whereas, in the second stage the predictive models were conducted. The results showed that XGBoost model attained the superior accuracy in comparison with other models by achieving the highest determination coefficient (R = 0.92). The research was extended to investigate the feature importance analysis which indicated that the initial concentration ratio of metals to biochar and pH were the most influential factors toward the adsorption efficiency followed by Pyrolysis temperature, while other features like physical properties as surface area and pore structure had a minimal effect on efficiency. These findings highlighted the importance of using ensemble ML models in guiding heavy metals removal solutions as it provides an efficient prediction and ease the selection of the environmental application.