Monte Carlo (MC) is a term quite common in the research community but, quite surprisingly, it may possess a different meaning among researchers in different areas. This paradox is derived from the potential of the method to serve as a modeling tool of systems that belong to a very wide range of different areas in science, such as mathematics, biology, economics, and physics. Thus, depending on the nature of the system under study and the type of the calculated properties, a different variation of the MC method may be encountered. In the present work, an attempt is made to provide a short overview of some of the most important MC techniques and their applications. Special emphasis is placed on problems related to the polymer science, with a more persistent presentation of a specific class of MC methods that can be implemented to the different scales of a polymerization process in order to predict the dynamic evolution of key properties of the polymer in terms of the varying process conditions. The latest developments in this field have made it possible to relate the macroscopic characteristics of a process to the end-use properties and functionalities of the produced polymeric products, via a detailed tracking of the evolution of the microstructural characteristics of the polymer chains. The presentation of these MC techniques is accompanied by specific illustrative examples from the literature.