The incorporation of hybrid renewable energy sources (RESs) into grid-integrated systems, comprising photovoltaic (PV) systems, wind turbines (WTs) and battery energy storage systems (BESSs), has become increasingly crucial in meeting global energy demands. In this paper, an enhanced adaptive neuro-fuzzy inference system (ANFIS)-based firebug swarm optimisation (FBSO) algorithm has been integrated with a unified power quality conditioner (UPQC) to mitigate power quality (PQ) issues in hybrid renewable energy systems (HRESs). An intelligent, adaptive and predictive control mechanism that combines machine learning (ANFIS) and optimisation (FBSO) is used in the adaptive neuro-fuzzy inference system-based firebug swarm optimisation (ANFIS-FBSO) framework to implement power balancing and frequency stabilisation. This allows for the dynamic management of energy flow and system stability in HRESs. The initial setup includes a WT, BESS and PV system connected to the load system. In HRESs, the primary objectives are to meet load demand and enhance PQ. 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 (SAPF), is used to address PQ issues in current and voltage, thereby enhancing UPQC performance. The FBSO algorithm optimises 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 (THDs). To assess the efficacy of the proposed technique, various cases are analysed and compared with existing methods.