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This abstract explores the use of soft computing techniques for harmonic mitigation in renewable energy integration, particularly for photovoltaic (PV) systems in power grids. Traditional methods often fail to effectively mitigate harmonics, leading to system performance compromise and grid instability. Soft computing's adaptability and ability to handle complex systems offer promising solutions. The study explores the use of soft computing techniques like artificial neural networks, fuzzy logic, and genetic algorithms to mitigate harmonics in PV systems. ANNs learn complex patterns, fuzzy logic handles uncertain data, and genetic algorithms optimize mitigation strategies for varying grid conditions and system dynamics. The study demonstrates the effectiveness of soft computing-based harmonic mitigation techniques in improving power quality indices like total harmonic distortion and voltage stability. It emphasizes the importance of integrating advanced computational approaches in PV systems for seamless grid integration and promoting sustainable renewable energy integration.