Industrial, agricultural, and pharmaceutical water pollution still poses world health and ecosystem risks. Adsorption as a form of remediation has become popular because it requires minimum operations and is efficient in most of the pollutants. Conventional adsorbents however present the drawbacks of low selectivity, regeneration and sustainability. Through nanotechnology, this review discusses the development of the next-generation adsorbents, which have been optimised through artificial intelligence (AI) and machine learning (ML) to address such challenge. We address the more sophisticated nanomaterials- metal-organic frameworks (MOFs), carbon-based nanostructures, magnetic nanoparticles and biochar composites, and their superior surface area or porosity adjustability, and bound pollutant capacity. Means of functionalization and structural amendments are presented to enhance capacity, selectivity and durability. Predictive modeling and optimization of the adsorbent performance in practice is possible because of the integration of AI/ML instruments such as ANN, SVM, DFT, and Monte Carlo simulations. The review also includes the most important barriers such as environmental risks of nanomaterials, regeneration inefficiencies, cost performance trade-offs, and regulatory limitations. Integration of advances in materials science, data-based modeling, and sustainable engineering, this paper provides a route map toward upscale and environmentally friendly wastewater remediation. Finally, nanotechnology powered and AI enhanced adsorbents are innovations that will take the world a long way forward in the provision of clean water globally.
Tabassam et al. (2025) studied this question.