Exploring innovative solutions to optimize the performance of existing energy systems and planning for future expansion and advancements is crucial [1]. Thus, the integration of artificial intelligence (AI) techniques, as physics informed neural networks (PINNs), and advanced computational methods in a digital twin concept is gaining momentum as a promising approach for the corresponding shift in operations strategies to support the growing energy demands and the inevitable transition towards sustainable and renewable-energy-centered production [2,3]. Hence, this article discusses how the application of digital twins coupled with AI-driven finite element modeling (FEM) is capable of real-time insights and optimization while maximizing efficiency and sustainability across numerous and distinct power system applications. Through this analysis, the digital twins advanced analytics impact on the energy sector knowledge creation, real-time monitoring, predictive maintenance and operational performance optimization while reducing lifecycle costs and minimizing environmental footprints, was highlighted. Employing this integrated technique, the energy industry achieved the development of “intelligent” and digitalized power systems while also ensuring efficiency, reliability and robustness in operation across the entire energy ecosystem.