The use of machine learning (ML) in catalysis has been significantly increased in recent years due to the astonishing developments in data processing technologies and the accumulation of a large amount of data in published literature and databases. The data generated in house or extracted from external sources have been analyzed using various ML techniques to see patterns, develop models for prediction and deduce heuristic rules for the future. This communication aims to review the works involving knowledge discovery in catalysis using ML techniques; the basic principles, common tools and implementation of ML in catalysis are also summarized.