The incorporation of hybrid renewable energy sources into grid integrated systems, comprising photovoltaic systems, wind tur-bines and battery energy storage systems has become increasingly crucial in meeting global energy demands. In this paper, an enhanced Adaptive Neuro-Fuzzy Inference System based Firebug Swarm Optimization (ANFIS-FBSO) algorithm has been inte-grated with a unified power quality conditioner to mitigate power quality issues in hybrid renewable energy systems. The initial setup includes a wind turbine, battery energy storage system and photovoltaic system connected to the load system. In hybrid renewable energy systems, the primary objectives are to meet load demand and enhance power quality. To achieve these goals, a Multi-Resolution Proportional-Integral-Derivative (MRPID) controller, alongside an ANFIS-FBSO based controller in series and a shunt active power filter, is employed to address power quality issues in current and voltage, thereby enhancing UPQC performance. The Firebug Swarm Optimization (FBSO) algorithm optimizes the learning function of the ANFIS for optimal outcomes. The proposed technique is implemented to validate its performance under various conditions, including voltage sag, current sag, real power, reactive power, and total harmonic distortions. To assess the efficacy of the proposed technique, various cases are analyzed and compared with existing methods.