Smart synergies: Integrating nanobiotechnology and artificial intelligence for sustainable water management: A critical review

Abstract

Water scarcity, pollution and, climate-driven environmental changes necessitate innovative approaches for sustainable water resource management. This review critically examines the emerging integration of nanobiotechnology and artificial intelligence (AI) for water quality monitoring, treatment optimization, desalination, wastewater management, and contaminant detection. Literature published between 2000 and 2025 was systematically surveyed using major scientific databases, including Scopus, Web of Science, ScienceDirect, PubMed, and Google Scholar. The review evaluates the roles of nanomaterials, nanosensors, nanomembranes, nanozymes, and bio-nano hybrid systems, alongside AI techniques such as artificial neural networks, convolutional neural networks, random forests, and long short-term memory models. Representative case studies are synthesized to highlight quantitative performance improvements, practical applications, and associated limitations. Key challenges, including data quality, sensor drift, membrane fouling, nanoparticle agglomeration, environmental risks, and regulatory gaps, are critically discussed. The review identifies current research gaps and future directions for developing intelligent, efficient, and sustainable water management systems through AI–nanobiotechnology convergence. Future development should therefore prioritize safe-and-sustainable-by-design materials, robust AI validation, life-cycle assessment, transparent governance, and integrated risk management.