A common data mining technique called frequent pattern mining involves finding repeating patterns in huge datasets. Big data processing for routine pattern mining is difficult, nevertheless, because of the complexity and volume of the data. The MapReduce programming model has become a well-liked method for handling large amounts of data, and numerous research have used it for frequent pattern mining. This review study looks at the literature on developing solutions for frequent MapReduce pattern mining on large data sets. The paper gives a general review of frequent pattern mining and MapReduce, explores the difficulties of frequent pattern mining on large data sets, and evaluates the advantages and disadvantages of the current methods for frequent pattern mining byMapReduce. The review paper also notes patterns and trends in the literature and explores their implications for the creation of solutions for routine MapReduce pattern mining on large datasets. Future field research recommendations are made in the paper's conclusion.