In analytical science, data extraction from complex or extensive datasets can be a laborious task that takes a long time to complete. Machine learning (ML) offers a pioneering opportunity to rapidly extract information from chromatography, spectroscopy, and mass spectrometry datasets, among others. Over the past few years, new approaches have been developed for the rapid processing of Raman spectra using ML. This review will discuss different applications of ML techniques employed in Raman.