<p>The increasing use of renewable energy, particularly photovoltaic (PV) systems, creates issues for grid stability and reactive power management. Variable loads and fluctuating solar irradiation can lead to voltage instability, power losses, and low quality power. Traditional energy storage and control strategies often struggle under changing system conditions. To address these issues, this paper proposes a hybrid method for improving grid stability reactive power management in PV and Superconducting Magnetic Energy Storage Inverters (SMES) using a hybrid approach. The proposed hybrid approach is a combined performance of both the Giraffe Kicking Optimization Algorithm (GKOA) and Higher-Order Topological Neural Networks (HOTNN), named GKOA-HOTNN. The primary aim of the proposed approach is to distribute the necessary reactive power from the PV and SMES power inverters locally. The proposed GKOA algorithm is employed to optimize the reactive power distribution and enhance the performance of grid stability in PV and superconducting magnetic energy storage systems. HOTNN is used to estimate the charging and discharging process of superconducting magnetic storage systems and electric vehicles (EVs). The performance of the proposed technique is evaluated and compared with other existing methods on the MATLAB platform, including Random Forest Cuckoo Search Optimization (RF-CSO), Particle Swarm Optimization (PSO), and Scaled Conjugate Artificial Neural Network (SC-ANN), proposed method achieves a power loss of 1&#xa0;kW and an efficiency of 98%, demonstrating superior performance in managing reactive power in PV-SMES systems. These results indicate that the approach effectively reduces power losses, improves energy efficiency, and enhances overall grid stability, making it a reliable solution for renewable-integrated utility grids.</p>