Abstract

Desalination is now being used more frequently to effectively address global water shortages and provide much-needed freshwater to arid regions and communities in need. Although there have been many improvements in technology at desalination plants, all the other stages involved in operating a desalination system are still affected by inefficiency, increased energy consumption, rising costs, and negative environmental impacts. Overcoming these problems requires improvement across the entire supply chain, rather than just at the plant level. This study assesses the effects of Machine Learning (ML) on enhancing the efficiency, robustness, and sustainability of desalination supply chains. For demand forecasting, supervised learning is utilised to detect deviations and optimise supply chain frameworks, which incorporate reinforcement learning, actual data, and trial situations. The integrated ML has reduced downtime by 18%, improved product distribution by 12%, lowered operating expenses by 14.2%, and nearly halved the company’s carbon emissions compared to standard operations. The results confirm that ML encourages more than minor changes and has a significant impact on the water management process. Using Artificial Intelligence in desalination helps experts and planners meet the issues of increasing water use and sustainability worldwide. It introduces a fresh, multi-technique ML model that enhances water supply management and provides a pathway toward greener, more robust desalination methods, supporting the goal of sustainable water security.