This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output. / Doctor of Philosophy / The decreasing supply of freshwater sources has made desalination technology an attractive solution. Desalination—or the removal of salt from water—provides an opportunity to produce more freshwater by treating saline sources and recycled water. One prominent form of desalination is Reverse Osmosis (RO), an energy intensive process in which freshwater is forced from a pressurized feed through a semipermeable membrane. A significant limiting cost factor for RO desalination is the maintenance and replacement of semipermeable RO membranes. Over time, unwanted particles accumulate on the membrane surface in a process known as membrane fouling. Significant levels of fouling can drive up costs, negatively affect product quality (permeate water), and decrease the useful lifetime of the membrane. As a result, operators employ various fouling control techniques, such as membrane cleaning, to mitigate its effects on production and minimize damage to the membrane. This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output.