Communication is essential in health care, both among professionals and between professionals and their patients. And, even when presented in tabular or graphic form, well-written documents describing the information in structured databases may be easier to comprehend, more edifying, and even more convincing than the structured data. Artificial intelligence is now being used in various fields of content creation and content abstraction, and all this is possible due to a subset of AI called NLP, which can be expanded as Natural Language Processing. In this paper, the research work has aimed to focus on a particular category of NLP, which is NLG. It can be expanded as Natural Language Generation (NLG) is a software technique that leverages artificial intelligence (AI) to create written or spoken language that is human-like from either structured or unstructured input. Using techniques from the field of natural language generation, documents can be generated automatically from structured data. This paper combines NLG techniques with the stroke dataset, which contains the different factors that cause stroke in people, and to highlight the relationships between them. Stroke occurs when the flow of blood to the brain is slowed or entirely halted, preventing brain tissue from receiving oxygen and nutrients. The model has chosen a dataset from Kaggle that has those different factors that cause stroke as columns. In this study, the authors aim to bring in the concept of data storytelling, which will give a better understanding of the factors that cause brain stroke in individuals.