High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries, particularly in optimizing electrodes and electrolytes. This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems, which bridges the gap between artificial intelligence and electrochemistry. We also examine the potential challenges and opportunities associated with different deep learning approaches, tailoring them to specific battery requirements. Ultimately, we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology. Furthermore, we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.