In recent years, high entropy alloys have increasingly attracted attention due to their excellent properties and broad development prospects, become a hot field in materials science. Due to the complex element composition of high entropy alloy, it is difficult and expensive to use the traditional methods to calculate. The diversity of the influencing factors also makes the design of high entropy alloy more difficult. It is urgent to develop new strategies to accelerate the exploration of high entropy alloy composition space. With the development of research on high entropy alloys and the accumulation of experimental data, researchers try to find solutions from data. At the same time, the rise of artificial intelligence has dramatically changed our life. Machine learning and high entropy alloy field cross each other, and a series of achievements have been achieved. Artificial neural networks, support vector machine, principal component analysis, and other methods have been applied to analyzing and predicting high entropy alloys. Besides, machine learning combined with ab initio and thermodynamic library-based methods, has shown advantages in mining data value and guiding experimental design. In this paper, machine learning and high entropy alloys in materials science are briefly introduced, and recent researches on high entropy alloy design aided by machine learning are reviewed. Some prospects and suggestions for the application of machine learning in high entropy alloys in the future are put forward.