The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in data science could have a great impact on traditional catalysis research in both industry and academia and could accelerate the development of catalysts. Machine learning (ML), a subfield of data science, can play a central role in this paradigm shift away from the use of traditional approaches. In this review, we present a user's guide for ML that we believe will be helpful for scientists performing research in the field of catalysis and summarize recent progress that has been made in utilizing ML to create homogeneous and heterogeneous catalysts. The focus of the review is on the design, synthesis, and characterization of catalytic materials/compounds as well as their applications to catalyzed processes. The ML technique not only enhances ways to discover catalysts but also serves as a powerful tool to establish a deeper understanding of relationships between the properties of materials/compounds and their catalytic activities, selectivities, and stabilities. This knowledge facilitates the establishment of principles employed to design catalysts and to enhance their efficiencies. Despite such advantages of ML, it is noteworthly that the current ML-assisted development of real catalysts remains in its infancy, mainly because of the complexity of catalysis associated with the fact that catalysis is a time dependent dynamic event. In this review, we discuss how seamless integration of experiment, theory, and data science can be used to accelerate catalyst development and to guide future studies aimed at applications that will impact society's need to produce energy, materials, and chemicals. Moreover, the limitations and difficulties of ML in catalysis research originating from the complex nature of catalysis are discussed in order to make the catalysis community aware of challenges that need to be addressed for effective and practical use of ML in the field.