The development of advanced batteries has become an important area to meet the rapidly growing demands of modern applications, such as electric vehicles, energy storage, and portable devices. However, traditional design methods of batteries usually utilize computing-consuming numerical simulation and time-consuming real-world experiments, which have limited the exploration of modern batteries. The emerging deep learning algorithms have the ability to analyze complex and high-dimensional datasets, making it possible to change the game in battery design process. In this paper, we propose a comprehensive framework that combines deep learning (DL) algorithms with essential battery parameters to predict battery performance under different operational conditions. A DL model is established using the dataset from various battery experiments to accurately predict the key metrics, e.g., charge-discharge efficiency and lifecycle performance. By enhancing prediction accuracy, our method will increase design efficiency while achieving the required battery performance, which will drive innovation in battery technology and contribute to the development of next-generation energy storage systems.