As the global energy landscape shifts towards renewables, efficient and intelligent battery management will become increasingly crucial. Lithium-ion batteries, which play an important role in energy storage for electric vehicles and renewable power systems, confront sustainability and safety issues due to performance degradation, temperature instability, manufacturing flaws, and end-of-life disposal. Early-stage defects—such as electrode misalignment and material inconsistencies—can result in significant failures if undetected. In order to address these problems over the course of the battery lifecycle, this study investigates the revolutionary potential of AI-driven Digital Twins. Continuous monitoring, predictive maintenance, and performance simulation under various conditions are made possible by digital twins, which are virtual replicas that are synchronized with real-time data. They optimize lifecycle decision-making, system integration, and battery health diagnostics by combining artificial intelligence and IoT-enabled data streams. This paper investigates the taxonomy, topologies, and deployment tactics of battery-focused digital twins, comparing various AI and machine learning models (LSTM, CNN, GNN, Transformer) for State of Health (SoH), State of Charge (SoC), and Remaining Useful Life (RUL) prediction. The study examines current research trends, available datasets, and evaluation metrics (such as MAE, RMSE, and R2). This analysis shows how the advancement of intelligent battery management systems is consistent with the global Sustainable Development Goals (SDGs), notably in terms of boosting sustainable energy usage, responsible consumption. This work contributes to Corporate Social Responsibility (CSR) by advocating for smart technologies that extend battery life, reduce electronic waste, and enable greener corporate energy practices.