As the Battery Energy Storage System (BESS) industry accelerates alongside renewable energy adoption, advanced battery management is becoming essential. While traditional Battery Management Systems (BMS) provide critical safety and operational control, they often lack precision in State of Charge (SoC) estimates and offer limited insight into long-term battery health. Predictive analytics fills these gaps by leveraging real-world data to uncover manufacturing deficiencies and enhance accuracy in SoC and State of Health (SoH) metrics. This presentation explores how predictive analytics can drive smarter, more reliable energy storage, thus empowering stakeholders to optimize battery performance in a rapidly evolving, data-driven energy landscape.