Battery pack Remaining Useful Life (RUL) prediction stands at the crossroads of technology and sustain-ability in electrified transportation and energy storage. This review journeys through the landscape of RUL prediction, from the traditional empirical models to the cutting-edge machine learning techniques. It is a technical analysis and a narrative of evolution, challenges, and possibilities. The paper delves into the complexities of data quality, algorithm intricacy, and real-world applicability, casting a critical eye on the road ahead. It calls for collaboration, innovation, and a shared vision for a future where battery systems are efficient and resonate with our broader sustainability goals.