• Machine learning accelerates the discovery of high-performance cathode, anode, and electrolyte materials for solid-state batteries by mining extensive material databases.
• ML algorithms accurately predict key battery management system indicators, including state of charge, state of health, remaining useful life, and capacity.
• The review identifies critical challenges such as data quality issues and poor code portability, proposing solutions for future research.
• AI-driven approaches significantly shorten the research and development cycle for solid-state batteries, overcoming traditional trial-and-error inefficiencies.