As the demand for electricity continues to increase globally, there is a need for more efficient and reliable power systems. This paper presents a modeling and simulation study of an AI-based automated design concept for power systems. The proposed design concept utilizes machine learning algorithms to generate optimal power system designs based on user-specified objectives and constraints. The study focuses on a case study of a power distribution system, and the proposed AI-based automated design concept is implemented using a combination of Python programming language and the OpenDSS simulation software. The study evaluates the performance of the AI-based automated design concept against traditional design approaches in terms of various performance metrics such as power losses, voltage stability, and system reliability. The results show that the AI-based automated design concept outperforms the traditional design approaches in terms of power loss reduction, voltage stability improvement, and system reliability. Moreover, the proposed approach offers flexibility in terms of incorporating various objectives and constraints in the design process, which makes it suitable for different types of power systems. In conclusion, the proposed AI-based automated design concept offers a promising solution. The study spectacles that the use of machine learning algorithms can lead to more efficient, reliable, and costeffective power system designs. Future research can focus on extending the proposed approach to other types of power systems and exploring the potential of other machine learning techniques for power system design and operation.