Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries due to the
natural abundance, low cost, and wide geographic availability of sodium resources. However, their practical
implementation is hindered by challenges such as lower energy density, slower ion diffusion, and limited
cycle stability. In recent years, machine learning (ML) has been increasingly applied to accelerate the
discovery, design, and optimization of SIB materials and systems. This review provides a comprehensive
overview of ML applications in sodium-ion battery research, including electrode material discovery,
electrolyte optimization, performance prediction, and degradation analysis. Various ML techniques,
including supervised, unsupervised, and deep learning, are discussed in relation to their roles in materials
informatics. Additionally, challenges such as data scarcity, model interpretability, and transferability are
critically analyzed. Finally, future perspectives on integrating ML with high-throughput experiments and
quantum computing are highlighted to guide next-generation sodium-ion battery research.