Large Saudi corporations operate with substantial capital commitments, complex supplier and customer networks, exposure to commodity and interest-rate shocks, and increasingly digital finance functions. These conditions make cash flow optimization and liquidity risk reduction a dynamic decision problem rather than a static ratio-management exercise. This review synthesizes recent research on corporate cash holdings, working capital, trade credit, financial forecasting, machine learning, explainable artificial intelligence, and Gulf-region liquidity behavior to develop an integrated view of intelligent financial decision models. Evidence indicates that advanced forecasting can improve the timing and granularity of liquidity estimates, but prediction alone does not determine an economically optimal action. Effective models must connect forecasts to working-capital levers, liquidity buffers, financing choices, scenario constraints, and governance controls. Saudi and Gulf evidence further shows that cash policies respond to financial constraints, macroeconomic weakness, policy uncertainty, geopolitical risk, oil-price volatility, Shariah-related financing structures, and sector characteristics. The review therefore proposes a layered decision architecture that combines transaction-level data, probabilistic cash forecasting, optimization, stress testing, explainability, and managerial override. The synthesis argues that the most useful corporate treasury systems should optimize expected liquidity while preserving resilience under adverse scenarios and should measure forecast value by decision outcomes rather than prediction accuracy alone. Research priorities include Saudi-specific benchmarking, causal evaluation of treasury interventions, multi-entity liquidity optimization, interpretable reinforcement or prescriptive learning, and governance standards for model risk. The resulting framework offers a practical research agenda for large Saudi corporations seeking to reduce idle cash, shorten the cash conversion cycle, improve funding decisions, and protect operational continuity without creating opaque automated financial control.