<p>Implementing membrane fouling for the treatment of heavy metal–contaminated water is one of the critical challenges for ensuring efficient and sustainable purification. Increase in population, urbanization and industrialization leads to contamination of water sources. Chemicals, garbage, plastics and other pollution have suffocated the rivers, reservoirs, lakes and seas. Water contains both soluble/insoluble waste and it is very difficult to separate the soluble pollutants. Soluble heavy metals like mercury, lead and cadmium affect brain, heart, kidneys, lungs and immune system of people of all ages. Consumption of polluted water in India causes some of the deadly diseases like cholera, dysentery, diarrhea, tuberculosis, jaundice, etc. Not only human beings but also plants, animals, aquatic animals are affected by this pollution which leads to risks like toxicity, persistence in the environment and bio-accumulative nature. This article addresses AI/ML models, deep learning models and metaheuristic algorithms to predict fouling and optimize heavy metal removal through membrane filtration. These models enhance accuracy, decrease the load in experiments and optimize process conditions. Data quality, model interpretability and real-world generalizability still exist in essential gaps. This review suggests the significance of real-time monitoring and explains the need for integrating AI/ML models for wastewater treatment process.</p>