Global Trends In Maching Learning and Reverse Osmosi S: A Bibliometric Review To Address The Water Crisis and Climate Change For A Sustainable Future

The growing pressure on water resources and climate change has driven the search for technologies capable of ensuring safe and sustainable water. In this context, the correlation between reverse osmosis (RO) and machine learning (ML) emerges as one of the most promising fields for transforming water treatment and desalination systems. This study develops a bibliometric and analytical review of recent literature, identifying scientific patterns, dominant research lines, and key contributions in the application of intelligent algorithms within RO processes. The findings show rapid development in studies incorporating predictive models to anticipate membrane fouling, optimize energy use, and improve plant operation. Countries such as the United States, India, and China lead in scientific production and technological advances, standing out for their innovations in hybrid systems, advanced membranes, and data-driven autonomous control. Likewise, the most influential authors have strengthened perspectives that integrate renewable energies, artificial intelligence, and novel filtration architectures. In conclusion, the integration of ML with RO drives more efficient, predictive, and sustainable water systems to address the growing water scarcity.