Hardness prediction is progressively required for materials such as multicomponent alloys (MAs) due to the enormous space of mathematically possible MAs. This prediction is even more necessary in view of the waste of resources in trial-and-error approach. To deal with a great number of possibilities limited to the scarce information about very few experiments performed, machine learning has been increasingly used. Therefore, this paper briefly presents the state of the art in hardness prediction of multicomponent alloys using machine learning (ML), pointing out the relevance of the subject, recent progress, and possible trends. The introductory and literature review sections show the recent advance in the application of ML in hardness prediction of MAs, its scope, methods, and main findings. The bibliometrics throughout the last five years is presented in the results section. Consequently, an analysis related to the growth potential of this research field is conducted, highlighting possible related opportunities and trends. Lastly, the main conclusions are presented.