A superconducting saturated core reactor is an electromagnetic device that plays a fundamental role in advanced electrical systems, providing increased efficiency and improved dynamic control over power flow in transmission lines, while also acting as a fault current limiter. In the context of AI-powered Cyber-Physical Systems (CPS), the development of precise analytical models is essential to optimize performance and reduce reliance on computationally intensive simulations. This paper proposes an analytical modeling approach that combines with numerical simulation for a three-phase superconducting saturated core reactor (SSCR) in a laboratory-scale prototype. Modeling a saturated core reactor relies on its magnetic characteristic, specifically the linked flux versus AC current. This characteristic can be obtained either through experimental measurements or using the finite element method (FEM). In this study, the reactor’s magnetic characteristics are extracted using FEM from a 3D model of the laboratory-scale prototype developed in COMSOL Multiphysics employing A-formulation, where A represents the magnetic vector potential. The model is analyzed for different DC magnetization levels. An analytical equation is then fitted to the obtained data using MATLAB’s Curve Fitting Toolbox (cftool) to determine four key parameters for each DC magnetization current. These parameters are subsequently used to compute flux, current, and voltage in the reactor through an analytical approach, enabling a quantitative comparison between the analytical model and FEM – based simulations. The error evaluation between the models confirms the accuracy and applicability of the proposed methodology. The results demonstrate that the analytical model provides an efficient solution for integration into AI-driven Cyber-Physical Systems, offering significant potential for its application in the analysis, optimization, and real-time control of electromagnetic devices within advanced electrical grids.