The energy storage systems are an important basis for electric vehicles and electronic devices. The existing battery design based on machine learning is able to quickly connect the complex relationship among material microstructure, material properties, and battery macroscopic properties. This review represented the applications and prospects of machine learning in micro-material design and state estimation of batteries. The data sources of machine learning battery design, advantages and disadvantages of algorithms and their application scenarios in the battery field, related innovative work in recent years and their prospects were discussed. This review can provide a reference for machine learning in the macro-/micro-design of energy storage systems.