The field of AI Planning has undergone significant growth since the advent of the STRIPS planner in 1971, fueled by the need to tackle an expanding array of complex problem domains ranging from robotics to quantum computing. However, the burgeoning landscape of planners and tools raises concerns regarding software quality assurance amidst increasing complexity. Design patterns offer a promising avenue for addressing this concern, providing structured solutions to recurring design problems and enhancing software development processes. This thesis investigates the systematic identification of design patterns in AI Planning software, guided by a multi-step methodology inspired by Fehling et al. Through reverse engineering and pattern identification processes, this study explores the prevalence and applicability of design patterns across various AI Planning tools and categories. Our findings reveal the widespread utilization of certain patterns, such as Proxy and Factory patterns, reflecting their compatibility with commonly used programming languages. Surprisingly, no novel design patterns specific to AI Planning software were uncovered, highlighting the need for further research in this area. Additionally, the lack of dedicated architectural documentation in research papers emphasizes the importance of identifying effective design patterns to enhance the overall quality of AI Planning software development and maintenance processes.