Other variables can also be incorporated into the predictive models such as the initial concentration of heavy metals, the specific surface area of metal-organic frameworks (MOFs), and the presence of anions. A study used four tree-based ML models, including light gradient-boosting machine (LightGBM), XGB, gradient-boosted decision trees (GBDT), and RF, to predict the adsorption of arsenate [As(V)] by MOFs . Among these models, LightGBM yields the most accurate and reliable prediction with R 2 and RMSE values of 0.996 and 2.069, respectively. The sensitivity analysis indicates that the adsorption process is adversely affected by the initial As(V) concentration and is directly influenced by the specific surface area and dosage of MOFs. ANN and symbiotic organisms search (SOS) algorithm can predict the removal of five heavy metals (Al, Cd, Co, Cu, Fe, and Pb) by two adsorbents, Chitosan and Chitosan-Montmorillonite nanocomposite . First, RBF-ANN has a higher prediction accuracy than MLP-ANN for the two adsorbents and the five heavy metals. Second, when integrated with SOS algorithm, RBF-ANN facilitates the identification of optimal performance parameters and increases the adsorption performance than the experimental results. Finally, RBF-ANN outperforms Langmuir and Freundlich models when considering the five heavy metals and three operational parameters (pH, absorbent dose, and contact time).