Adequate management of cash flow is one of the key indicators of financial stability that lets organizations survive a volatile economy, address the issue of liquidity, and improve capital allocation. However, traditional cash flow forecasting models do not often consider rapid market shifts, economic shocks, and unpredictable industry-specific risks. With the help of data, Machine Learning (ML) brings the power of these algorithms into financial management. It transforms scenario planning to provide better insights, predictive models, and better decision-making capabilities in real time. The research provides insights into the power of AI-based financial tools, probabilistic forecasting, and scenario modeling enabling organizations to embed agility in dealing with uncertainties in a better way. Machine learning allows companies to increase accuracy in predictive analysis, while scenario planning provides a structured and well-conceived framework to think through potential financial outcomes. These complementary innovations are transitioning organizations from reactive-to-proactive cash flow strategies, improving resilience for economic instability. This study employs financial theories and methodologies to describe the iterative process involved in AI-powered analytics, scenario planning, and adaptive liquidity management. Drawing from insights in prior research and financial models, this paper highlights machine learning and scenario planning as critical to financial resilience.