As the world is moving towards electric vehicles , we are observing a wide use of Lithium-Ion batteries in modern transportation. Lithium-Ion Batteries offer several advantages over conventional battery systems, including higher energy density that is energy stored per unit mass, longer Cycle Life, faster Charging rates, low Self-Discharge, lighter weight, and ease of maintenance as the memory effect present in other batteries is absent . Of course, challenges remain, e.g. accurate battery State of Health (SOH) estimation which stays as an indispensable parameter for safety and useability. Inaccurate SOH estimation can lead to unexpected vehicle behavior and a degraded end-user experience, especially due to incorrect "distance to empty" predictions. Also, different battery chemistries present various challenges, creating need of tailored models. In this paper, different SOH estimation techniques are reviewed and compared in detail . The SOH estimation approaches are broadly classified into three main categories: Model based estimation techniques, data driven estimation techniques, and fusion technology typically involving the combination of multiple estimation methods. This review highlights the strengths and limitations of each technique, offering a comparative analysis that enables researchers and engineers to select the most suitable approach based on system requirements and application constraints. Additionally, the trade-offs required for the transition from laboratory to field-use are discussed. this paper emphasizes the importance of reliable SOH estimation in enhancing the safety, longevity, and performance of battery-powered systems, and discusses potential future directions for developing more accurate, adaptive, and real-time SOH estimation frameworks. A robust SOH framework can reduce warranty costs for manufacturers, prevent thermal runaways by timely identifying degradation patterns, and improve user trust in E-vehicle technology.