AI-ML innovations in membrane technology: current progress and future prospects

Membrane-based separation processes are indispensable to desalination, water purification, gas separation, solvent recovery, and emerging sustainability-driven technologies; however, their advancement remains constrained by intrinsic trade-offs between permeability and selectivity, persistent fouling, complex multiscale transport phenomena, and the inherently slow, empirical nature of membrane material discovery and process optimization. Recent advances in artificial intelligence (AI) and machine learning (ML) have introduced a paradigm shift from trial-and-error experimentation toward predictive and data-driven engineering across molecular, material, and process scales. This review systematically examines the integration of AI/ML throughout the membrane design-to-deployment framework, including molecular design, membrane fabrication, performance prediction, fouling analysis, and intelligent process control. Particular emphasis is placed on physics-informed ML (PIML), interpretable learning frameworks, digital twins, and hybrid multiscale models that bridge molecular-level phenomena with industrial-scale membrane performance. The review further discusses emerging AI-driven strategies, including explainable AI (XAI), digital twins, and closed-loop experimentation, for enabling inverse membrane design, autonomous optimization, and next-generation smart membrane processes. Despite substantial progress, several critical challenges remain, including limited and non-standardized datasets, poor model transferability across operating conditions, insufficient interpretability, and the gap between computational predictions and experimentally realizable membrane systems. Finally, future perspectives toward autonomous and self-optimizing membrane platforms integrating predictive modelling, real-time sensing, and adaptive control are outlined, highlighting the transformative potential of AI/ML in accelerating innovation and deployment of next-generation sustainable separation technologies.