The increasing integration of photovoltaic (PV) systems into microgrids demands high voltage gain conversion and efficient maximum power extraction under dynamic environmental conditions. Conventional boost converters with classical MPPT algorithms suffer from limited gain, slow tracking speed, and steady-state oscillations, thereby reducing overall system efficiency and stability. This paper proposes a Radial Basis Function Neural Network (RBFNN)-based Maximum Power Point Tracking (MPPT) scheme integrated with a Quadratic Z-Source Boost Converter (QZSBC) for a PV-based microgrid system. The proposed converter topology offers enhanced voltage boosting capability, reduced switching stress, and improved reliability compared to conventional structures. The RBFNN controller ensures rapid convergence and accurate power tracking under varying irradiance and temperature conditions. The boosted DC output is interfaced with the microgrid through a three-phase Voltage Source Inverter employing dq control for harmonic mitigation and decoupled active–reactive power regulation. Simulation confirms improved dynamic response, reduced voltage ripple, and higher overall conversion efficiency.