{"id":10935,"date":"2026-09-21T18:08:02","date_gmt":"2026-09-21T18:08:02","guid":{"rendered":"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/?p=10935"},"modified":"2026-09-24T19:17:28","modified_gmt":"2026-09-24T19:17:28","slug":"pjc-1826","status":"publish","type":"post","link":"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/2026\/09\/21\/pjc-1826\/","title":{"rendered":"Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"10935\" class=\"elementor elementor-10935\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3424bd8 e-flex e-con-boxed e-con e-parent\" data-id=\"3424bd8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-f37598a e-con-full e-flex e-con e-child\" data-id=\"f37598a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2725e77 elementor-widget elementor-widget-text-editor\" data-id=\"2725e77\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<table border=\"0\" width=\"100%\" cellspacing=\"0\" cellpadding=\"0\" align=\"center\"><tbody><tr><td><div><strong>Review Article| Open Access<\/strong><\/div><div>Volume 16 | Issue 04 | Page 187-201| <a href=\"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/wp-content\/uploads\/sites\/2\/2026\/09\/PJC.v16.i04.p18.pdf\">https:\/\/doi.org\/10.15228\/2026.v16.i4.p18<\/a><\/div><\/td><\/tr><\/tbody><\/table><h4><strong><b>Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review<\/b><\/strong><\/h4><p><a href=\"https:\/\/orcid.org\/0009-0004-3962-5187\" target=\"_blank\" rel=\"noopener\"><strong>Faisal Latif<\/strong><\/a><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><p><strong>Muhammad Bilal<\/strong><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><p><strong>Muhammad Hasnain<\/strong><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><p><strong>Huzaifa<\/strong><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><p><strong>Mahnoor Saeed<\/strong><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><p><strong>Muhammad Ali<\/strong><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><p><strong>Raziya Nadeem<\/strong><br \/>Department of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan<\/p><div><table border=\"0\" width=\"93%\" cellspacing=\"0\" cellpadding=\"0\" align=\"center\"><tbody><tr><td align=\"center\" valign=\"top\"><strong>Received<\/strong><br \/>12 August, 2026<\/td><td align=\"center\" valign=\"top\"><strong>Accepted<\/strong><br \/>19 September, 2026<\/td><td align=\"center\" valign=\"top\"><strong>Published<\/strong><br \/>21 September, 2026<\/td><\/tr><\/tbody><\/table><div><p><strong>ABSTRACT:<\/strong><\/p><p>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&#8217;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.<\/p><p><strong>Keywords:\u00a0Machine learning, Data science, Heavy metal remediation, Predictive Modelling, Artificial neural networks<\/strong><\/p><p><strong>How to Cite this paper?<\/strong><\/p><\/div><div><p><strong>APA- Style<\/strong><br \/>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 <a href=\"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/wp-content\/uploads\/sites\/2\/2026\/09\/PJC.v16.i04.p18.pdf\"><em>Pakistan Journal of Chemistry<\/em>, 16(4), 187-201.<\/a> https:\/\/doi.org\/10.15228\/2026.v16.i4.p18.<\/p><p><strong>ACS Style<\/strong><br \/>F. Latif, M. Bilal, M. Hasnain, Huzaifa, M. Saeed, M. Ali, and R. Nadeem, <em>Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review<\/em> <a href=\"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/wp-content\/uploads\/sites\/2\/2026\/09\/PJC.v16.i04.p18.pdf\"><em>Pakistan Journal of Chemistry<\/em>, 16(4), 187-201.<\/a> https:\/\/doi.org\/10.15228\/2026.v16.i4.p18.<\/p><p><strong>AMA Style<\/strong><br \/>F. Latif; M. Bilal; M. Hasnain; Huzaifa; M. Saeed; M. Ali; and R. Nadeem; <em>Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review<\/em> <a href=\"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/wp-content\/uploads\/sites\/2\/2026\/09\/PJC.v16.i04.p18.pdf\"><em>Pakistan Journal of Chemistry<\/em>, 16(4), 187-201.<\/a> https:\/\/doi.org\/10.15228\/2026.v16.i4.p18.<\/p><p><strong>Chicago\/Turabian Style<\/strong><br \/>F Latif, M Bilal, M Hasnain, Huzaifa, M Saeed, M Ali, and R Nadeem, <em>Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review (2026)<\/em> <a href=\"https:\/\/chempublishers.com\/Pakistan-journal-of-chemistry\/wp-content\/uploads\/sites\/2\/2026\/09\/PJC.v16.i04.p18.pdf\"><em>Pakistan Journal of Chemistry<\/em>, 16(4), 187-201.<\/a> https:\/\/doi.org\/10.15228\/2026.v16.i4.p18.<\/p><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" rel=\"license\"><img decoding=\"async\" src=\"https:\/\/i.creativecommons.org\/l\/by\/4.0\/88x31.png\" alt=\"Creative Commons License\" \/><\/a><\/p><\/div><div><p>This work is licensed under a\u00a0<a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" rel=\"license\">Creative Commons Attribution 4.0 International License<\/a>.<\/p><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-17a3899 e-con-full e-flex e-con e-child\" data-id=\"17a3899\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Review Article| Open AccessVolume 16 | Issue 04 | Page 187-201| https:\/\/doi.org\/10.15228\/2026.v16.i4.p18 Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review Faisal LatifDepartment of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan Muhammad BilalDepartment of Chemistry, University of Agriculture, 38040, Faisalabad, Pakistan Muhammad HasnainDepartment of Chemistry, University [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[66,69],"tags":[],"class_list":["post-10935","post","type-post","status-publish","format-standard","hentry","category-66","category-issue-4"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives: A Review - Pakistan Journal of Chemistry<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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