The rapid development of deep learning (DL) algorithms has stimulated materials discovery by combining accurate first-principle simulations, autonomous synthesis, and testing experiments. DL models can predict electronic, thermodynamic, and mechanical properties of the battery and its materials effectively, given a finite number of training datasets generated from experiments and/or simulations. Integrating DL models into traditional experiments and simulations reduces the time and cost required for discovering and characterizing new electrode/electrolyte materials and additives for high-performance batteries, such as large capacity, high cycle life, strong mechanical strength, and improved safety. This contributes to the promising trend of electrification. In this review, we first elucidate basic algorithms in DL and then review recent applications of DL in electrode and electrolyte design. We aim to bridge the gap between the battery design and artificial intelligence (AI) and inspire scientific and technological developments in materials science, computer science, and engineering to harness the battery's potential for sustainability.