The integration of artificial intelligence and software engineering has been a key focus of research in recent years. In this paper, we present a new approach that utilizes the capabilities of natural language processing (NLP) techniques to automate the conversion of user stories into Unified Modelling Language (UML) diagrams transformation process such as use case, this approach addresses the limitations of manual conversion by improving accuracy and efficiency which are more formal and structured descriptions of user needs that can be easily mapped to UML diagrams. Among Agile requirement engineering practices, converting user stories into UML diagrams often relies on Model-Driven Architecture (MDA), where the user story is depicted at the highest abstraction level as a Computable—Independent Model (CIM) which is the highest level of describing the requirement, Our primary objective in our model is to showcase various methods for extracting a diagram from the Platform-Independent Model (PIM) based on user stories described in the Computational Independent Model (CIM) using NLP techniques. Furthermore, we conducted a thorough comparison of our method with existing approaches across three real-world scenarios. Our analysis revealed enhancements in detecting use cases by 3% and relationships by 8%. Moreover, we optimized the extraction process to better identify extend and include relationships, thereby improving the accuracy, comprehensiveness, and clarity of the resulting UML use case models.