This chapter reviews applications of machine learning (ML) to redshifted 21 cm cosmology, focusing on cosmic dawn, the Epoch of Reionization, and SKA-Low science. The redshifted 21 cm line directly probes diffuse neutral hydrogen, but the measured signal is not a simple astrophysical observable: density, ionization, heating, radiation backgrounds, foreground treatment, and instrumental response are coupled. The chapter first summarizes the physical ingredients needed in later sections, including the global signal, spatial fluctuations, morphology-sensitive statistics, and the 21 cm forest. It then discusses the main barriers to interpretation: bright foregrounds, radio-frequency interference, ionospheric and calibration effects, incomplete sampling, and the cost of forward modeling in high-dimensional parameter spaces. ML applications are organized by their role in the analysis chain. Observation-domain methods act on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological parameters.