This research explores the development of predictive analytics to optimize financial storage management in fintech applications. As user bases and data volumes grow, managing storage efficiently without incurring excessive costs poses a significant challenge. The methodology involved collecting relevant data, including transaction volumes and seasonal patterns, and employing predictive models such as time series and machine learning. The trained models were deployed in a live environment to forecast storage demands and issue alerts to technical teams. Results demonstrated improved capacity planning, seamless performance during peak periods, and cost savings by eliminating unnecessary storage expenditures, thereby enhancing user experience and operational efficiency.