<p>In the power grid, the integration of renewable energy often results in power quality complications, such as voltage swells, sags, and distorted harmonics, which impact the electrical system and affect customers. These complications are raised due to improper control constraint tuning. In this work, an innovative Cuckoo Search-based Bidirectional Recurrent Neural Network (CSbBRNN) is designed to mitigate power loss in solar power systems. Initially, a three-phase grid-linked PV array with renewable energy sources, controllers, and inverters was primarily developed in the MATLAB/Simulink platform. The cuckoo fitness was enabled in the dense layer of recurrent Nets to monitor the grid status. Here, the cuckoo fitness indicates the best solution of the cuckoo optimal algorithm, that is, laying an egg in the other's nest. Additionally, the grid system features both high and low voltage gain to accommodate the Cuk filter used. To achieve the optimal outcome, the Cuk filter's control parameters, such as duty cycle, capacitance, and inductance, are tuned by the cuckoo's best solution. It can result in reduced power loss and distortion from harmonics compared to other traditional filters. In this way, the power quality issues were mitigated, which is the prime objective of this study. The proposed method reduced the harmonic distortion to 0.1417 power unit (pu) and the Power loss to 0.5452 pu. Compared to other models, the proposed model has reduced power loss and total harmonic distortion (THD) by 2pu The impact of this present work is that it can offer an optimized solution for power quality issues in real-time renewable grid systems, helping to provide an uninterrupted power supply. In practice, the proposed strategy can benefit from reducing power quality issues in renewable systems, such as solar or wind electrical grid systems, which helps ensure a stable power supply.</p>