<p>The multiscale nature of battery materials and their accompanying electrochemical processes poses a fundamental challenge in theoretical modeling. Conventional methods often involve a trade-off between accuracy and computational efficiency, which constrains simulations across electronic, atomistic, and primitive particle-based resolutions and consequently limits the predictive capability of multiscale modeling. The integration of artificial intelligence (AI) is transforming theoretical modeling for battery performance prediction. Beyond offering incremental improvements, AI enables quantum-mechanical accuracy at the computational cost of classical force fields, infers cross-scale physical laws from atomistic simulations, and constructs scalable bridges across traditionally segregated modeling domains. By holistically managing the workflow from machine learning assisted material design to automated synthesis and systematic optimization, AI effectively addresses cross-scale modeling challenges. In this review, we outline simulation methodologies at diverse resolutions, chosen as paradigmatic representations of multiscale modeling, and explore the transformative role of AI in advancing battery research across these hierarchical scales. Building upon these methodological advances, we critically assess the application of AI-enhanced theoretical frameworks to tackle key challenges in battery science, underscoring AI’s pivotal role in connecting theoretical simulations with experimental synthesis and accelerating the rational design of next-generation battery systems. Finally, we discuss the current limitations and key challenges facing AI-facilitated multiscale modeling.</p>