<p>This work investigates a novel metaheuristic design of an adaptive control technique to enhance the compensation capability of the dynamic voltage restorer (DVR). The proposed artificial neural network (ANN) is integrated with Corona virus herd immunity optimizer to extract the fundamental quantity from polluted grid. The optimally developed Adaptive neuro-fuzzy interface system (ANFIS) model is implemented for dc voltage regulation under dynamics. The drawback of traditional system SRF-PI has manual intervention for tuning and mathematical modeling. The conventional SFR-PI-based DVR is not a failsafe mechanism for supply voltage variation conditions. In the proposed design of DVR, Feedforward-ANN interconnected weights are optimized by Corona virus herd immunity algorithm for forecasting the fundamental weight quantity and ANFIS is optimized by Hybrid learning to improve the performance of controller. The model performance is evaluated by employing the statistical indices such as mean square error, root mean square error, mean error, standard deviation and Regression (<i>R</i>). ANFIS controllers are used to regulate DC and AC voltage link and its efficacy is measured with improved rise time (<i>T</i>_r), fast settle time (<i>T</i>_s) and less overshoot of 0.074, 6.46%, and 0.24&#xa0;s, respectively. Compared to the conventional control strategies, ANN-ANFIS-based DVR performs better by improving the output voltage profiles and producing less THD of 2.13%. The simulation and experimental results are discussed to measure the efficacy of proposed control scheme.</p>