Artificial intelligence guided nanomaterials for sustainable environmental remediation advances and future perspectives

Abstract Environmental pollution by heavy metals, pharmaceuticals, and emerging contaminants has been identified as a serious global issue owing to their persistence, toxicity, and difficulty of removal using conventional methods. AI is changing the paradigm of nanotechnology through the rapid design and optimization of nanomaterials that can be used for environmental remediation. The systematic review covers recent advancements made in AI-driven nanotechnology through analysis of the literature obtained from Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, and Google Scholar databases. According to the PRISMA 2020 methodology, 816 records from the year 2015 until 2026 were found in total, but only 202 peer-reviewed papers were eligible for inclusion after systematic screening process. Remarkably, most of the selected papers have been published during the years of 2023 to 2026. The review highlights, the paper explains how ML, DL, ANNs, XAI, GNN, Bayesian optimization, RL, and autonomous robotic systems are impacting the field of nanomaterials design and application in environmental cleanup. Conclusions reveal that AI greatly enhances predictive capability, detects crucial material properties, optimizes processes of synthesis and operation, lowers the costs and time required for experiments, and expedites the creation of high-performing nanomaterials. In addition, new trends in the field of materials informatics, autonomous experimentation, digital twin technology, and generative AI are addressed as the next-generation tools for intelligent materials development. However, despite all achievements, problems related to the quality of data used, standardized datasets, model interpretability, scalability, reproducibility, and environmental safety represent significant hindrances in the practical application of AI in the area under discussion. This review reveals the revolutionary role of AI-driven nanotechnology in creating sustainable and intelligent technologies for environmental remediation.