Mathematical and logical reasoning is an important component of human intelligence. Thus, a common metric for evaluating Large Language Models (LLMs) is their ability to solve mathematical problems. Recently, LLMs have shown remarkable performance in completing various tasks such as text generation, text understanding and image analysis. Their mathematical and reasoning ability has also advanced rapidly, allowing them to solve complex algebra problems. However, LLMs still exhibit limitations in describing and reasoning about geometric and spatial concepts, failing to accurately identify and understand the logic within geometric figures. In order to address this gap in understanding, numerous diverse datasets of geometric figures and metadata are needed to continue training their geometric reasoning capabilities. In this research paper, we introduce an innovative algorithm to create synthetic polygon geometric shape datasets, and define methods to integrate synthetic geometric images and metadata into major LLMs for training, validation, and evaluation of their geometric reasoning abilities.