D.Swetha, Ragiri Manisha, R.Srilekha 02 06 Cash management is a critical function within financial institutions ensuring optimal liquidity efficient fund allocation and secure transaction processing Traditional cash management systems often rely on static rulebased frameworks and manual interventions which limit their ability to handle realtime financial complexities forecast cash flows accurately and detect anomalies or fraud With the increasing volume velocity and variety of financial data there is a growing need for intelligent systems that can adapt predict and automate cash management processes This study explores how Artificial Intelligence AI Machine Learning ML and Deep Learning DL can be leveraged to build smarter datadriven cash management systems AI algorithms are used for realtime decisionmaking in cash forecasting liquidity risk assessment and transaction prioritization ML models such as Random Forest and Gradient Boosting are employed to predict cash inflows and outflows based on historical patterns business cycles and external economic indicators DL models particularly Long ShortTerm Memory LSTM networks are utilized for sequence prediction in timeseries financial data enhancing the accuracy of cash flow forecasting Additionally anomaly detection techniques are integrated to identify potential fraud or operational errors ensuring system integrity and complianceBy combining these intelligent technologies the proposed system enhances operational efficiency reduces human error and improves financial planning accuracy This AIdriven framework represents a transformative step toward building fully automated adaptive and secure cash management systems that align with the dynamic needs of modern financial institutions Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026