One of the major obstacles in machine learning is a lack of data, both in terms of quality and quantity. With the end goal being the creation of data to be used for machine learning models for analyzing a variety of charts, this project implements a data synthesizer program that automates the generation of 30,000 diverse plot images using fifteen different plot types. The output of the program is used for the training of a generative model that translates the plots into new image samples, which further remedies the concern for data of adequate quality and quantity. The problem of generating an abundance of distinct plot images was divided into the production of diverse metadata and the combining of style customizations offered by two chosen graphing software tools. The results were overall successful, with vivid illustrations of plots generated by the data synthesizer presented and discussed.