Manual disassembly of increasing number of electric vehicle batteries (EVBs) is challenging due to fire, electric shock, toxic gas risks, and inefficiencies. This paper explores applying machine vision and machine learning to enhance robotized disassembly, improving efficiency, safety, and adaptability. Through advanced sensor data and intelligent algorithms, robotic systems can handle the complex and variable designs of EVBs, ensuring precise and safe disassembly. This approach offers a scalable, automated, and cost-effective solution for sustainable end-of-life EVB processing.