Power quality issues such as voltage sags, swells, and harmonics that hinder efficient energy transfer in grid-connected systems with sensitive loads lead to grid instability and potential load damage. This study proposes an improved hybrid power architecture that integrates grid and precise load management in a fuel cell system with a maximum power algorithm, performing pulse width modulation (PWM) prediction using an offline-trained deep learning method from dynamic voltage restorer (DVR) data. Grid-injected current and voltage are regulated using proportional–resonant and phase-locked loop (PR-PLL) controllers, while the DVR uses PLL-based synchronization and hysteresis control. Additionally, voltage and switching-signal data are processed with a recurrent neural network (RNN) to enable predictive analysis and estimation of inverter PWM patterns. The proposed data-driven control method aims to reduce the instantaneous processing load and dynamic response times of conventional controllers, thereby achieving high power quality and efficiency in renewable energy integration. The system’s success has been verified by testing it under temperature variations and sudden voltage fluctuations. The DC-link voltage was regulated at 400 V, current harmonic distortion was limited to approximately 5%, a power factor above 0.98 was achieved, and the sensitive load voltage remained at its nominal value despite voltage sags and swells. The mean squared error (MSE) was 0.0251 in the RNN method applied for inverter PWM estimation and analysis.