In this case study, we delve into the prospective benefits of deploying machine learning algorithms for optimizing liquidity management, especially forecasting cash flow requirements based on historical data and adaptive learning models. By replacing traditional rule-based or manual processes, we can help financial institutions augment liquidity management, rectify excess or shortfall scenarios, and circumvent risks associated with missed opportunities, delayed transactions, regulatory fines, and strained correspondent banking relationships.