Application of Machine Learning and Data Science in Heavy Metal Remediation: Advances, Challenges, and Future Perspectives

Heavy metal contamination of soil and water continues to pose persistent environmental and public health challenges, particularly in regions affected by rapid industrialization, mining, and intensive agriculture. Conventional remediation strategies—such as chemical precipitation, adsorption, soil washing, and bioremediation—have contributed significantly to pollution control; however, their implementation often relies on empirical optimization, prolonged experimentation, and site-specific trial-and-error approaches. These limitations restrict scalability, increase operational costs, and slow the development of sustainable remediation solutions. In recent years, the integration of machine learning (ML) and data science has emerged as a transformative direction in environmental engineering, offering predictive, data-driven alternatives to traditional remediation planning. This review critically examines the application of supervised, unsupervised, and deep learning models in metal remediation systems. Emphasis is placed on regression algorithms, artificial neural networks, support vector machines, ensemble techniques, clustering methods, and advanced deep neural architectures that enable prediction of metal removal efficiency, optimization of operational parameters, and modeling of adsorption and kinetic behaviors. The review further explores how data science workflows—including data acquisition, preprocessing, feature engineering, and multi-source data integration—support robust environmental decision-making. Particular attention is given to machine learning applications in bioremediation and phytoremediation, where predictive modeling enhances understanding of microbial performance and plant–metal interactions while reducing experimental time and cost. Despite promising advancements, significant challenges remain, including data scarcity, model interpretability, overfitting risks, lack of standardized environmental datasets, and computational constraints. Addressing these issues will require integration with real-time monitoring systems, Internet of Things (IoT) technologies, explainable artificial intelligence (XAI), and global environmental databases. The review concludes that transitioning from empirical remediation frameworks to predictive and adaptive systems represents a crucial step toward sustainable, scalable, and intelligent heavy metal management strategies. By synthesizing current developments and identifying research gaps, this article provides a comprehensive foundation for future interdisciplinary innovation at the intersection of environmental science, machine learning, and sustainable remediation engineering.