In the context of the transformation of the electric power industry—driven by the growing share of distributed generation, renewable energy sources, digitalization of consumers, and increasing complexity of grid topology–the need to ensure stable and high - quality power supply modes becomes increasingly critical. Of particular importance is the task of maintaining acceptable voltage levels at supply centers, as even minor deviations can lead to reduced reliability and efficiency of power system operation. Traditional voltage regulation approaches based on static calculations and manual decisions are becoming less effective under conditions of high variability in load and generation. This study focuses on the development of an automated system for selecting control actions capable of achieving the required voltage levels at network nodes in real time, taking into account the current state of the system. The article employs modern methods of intelligent data analysis, digital modeling, and elements of expert systems to enhance the speed, accuracy, and adaptability of decision - making (PyQt, NumPy, Docker). The implementation of such approaches not only minimizes the risk of operating condition violations but also contributes to increased energy efficiency, reduced operational costs, and the reliable operation of the power system as a whole.