ABSTRACT:

Heavy metal contamination of soils and water remains a persistent global environmental and public health challenge, exacerbated by industrialization, mining, and intensive agricultural activities. Although conventional remediation technologies, including chemical precipitation, adsorption, and bioremediation, have proven effective, optimizing them often relies on empirical, site-specific trial-and-error approaches, which limit scalability and increase operational costs. Machine learning (ML) and data science are increasingly proposed as transformative tools for predicting and optimizing remediation performance; however, their practical contribution and field-scale reliability remain insufficiently and critically assessed. The current review evaluates supervised, unsupervised, and deep learning approaches, including regression models, artificial neural networks, support vector machines, ensemble methods, clustering algorithms, and deep neural architectures, for predicting metal-removal efficiency, optimizing operational variables, and Modelling adsorption and remediation kinetics. Beyond reporting predictive performance, the review critically examines how well current ML models provide mechanistic insight, remain transferable across heterogeneous environmental conditions, and outperform conventional statistical and process-based approaches. It focuses on data quality, feature selection, validation strategies, multi-source data integration, and the risks of overfitting and data leakage. The review also assesses ML’s potential to advance bioremediation and phytoremediation by resolving complex microbial nutrient contaminant interactions and reducing experimental requirements. It also identifies limited, non-standardized datasets; limited interpretability; inadequate external validation; and poor cross-site generalisability as major barriers to real-world implementation. Future research should therefore prioritize explainable and physics-informed ML, robust uncertainty quantification, IoT-enabled real-time monitoring, and interoperable environmental databases to develop scientifically defensible, scalable, and adaptive remediation decision-support systems.

Keywords: Machine learning, Data science, Heavy metal remediation, Predictive Modelling, Artificial neural networks

How to Cite this paper?

APA- Style
Latif F., Bilal M., Hasnain M., Huzaifa, Saeed M., Ali M., and Nadeem R., (2026) Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review Pakistan Journal of Chemistry, 16(4), 187-201. https://doi.org/10.15228/2026.v16.i4.p18.

ACS Style
F. Latif, M. Bilal, M. Hasnain, Huzaifa, M. Saeed, M. Ali, and R. Nadeem, Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review Pakistan Journal of Chemistry, 16(4), 187-201. https://doi.org/10.15228/2026.v16.i4.p18.

AMA Style
F. Latif; M. Bilal; M. Hasnain; Huzaifa; M. Saeed; M. Ali; and R. Nadeem; Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review Pakistan Journal of Chemistry, 16(4), 187-201. https://doi.org/10.15228/2026.v16.i4.p18.

Chicago/Turabian Style
F Latif, M Bilal, M Hasnain, Huzaifa, M Saeed, M Ali, and R Nadeem, Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review (2026) Pakistan Journal of Chemistry, 16(4), 187-201. https://doi.org/10.15228/2026.v16.i4.p18.

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