Feature selection is imperative in machine learning and data mining when we have high-dimensional datasets with redundant, nosy and irrelevant features. The area of feature selection deals reducing the dimensionality of data and selecting only the most relevant features to increase the classification performance and reduce the computational cost. This problem has exponential growth, which makes it challenging specially for datasets with a large number of features. To solve this problem, a wide range of optimization algorithms are used of which grey wolf optimizer (GWO) is a recent one. This book chapter provides a brief review of the latest works on feature selection using GWO.