Since the commercialization of lithium-ion batteries in the 1990s, batteries based on alkali metals have been promising candidates for energy storage. The performances of these batteries, in terms of cost-efficiency, energy density, safety, and cycle life need continuous improvement. Battery performances are highly dependent on electrode materials, yet the long experimental period, intensive labor, and high cost remain bottlenecks in the improvement of electrode materials. Machine learning (ML), which is being increasingly integrated into materials science, offers transformative potential by reducing the R&D period and cost. ML also demonstrates significant advantages in the performance prediction of various materials, and it can also help reveal the structure-performance relationship of materials. ML-assisted material design and performance prediction thus enable the innovation of advanced materials. Herein, implementation of ML for exploring alkali metal-based batteries is outlined, highlighting various ML algorithms as well as electrode reaction mechanisms.