The integration of Artificial Intelligence (AI) into water treatment, specifically through the reverse osmosis (RO) process, significantly enhances efficiency, operational effectiveness, and cost savings. This review explores how AI technologies, particularly machine learning algorithms, are applied to optimize RO processes by predicting and managing membrane fouling, reducing energy consumption, and improving overall system performance. AI algorithms analyze historical data and real-time sensor inputs to predict membrane fouling, enabling proactive maintenance and reducing downtime. Additionally, AI optimizes energy usage by dynamically adjusting pump operations and pressure settings, ensuring efficient performance under varying conditions. Real-time monitoring and anomaly detection by AI systems enhance the reliability and responsiveness of RO processes, maintaining consistent water quality and enabling quick intervention when issues arise. Case studies demonstrate the practical benefits of AI in RO systems, including reduced maintenance costs, improved operational efficiency, and better resource utilization. The integration of AI in reverse osmosis processes thus offers significant advancements in sustainable water management, addressing global water demand challenges.