This research endeavors to enhance the performance and efficiency of photovoltaic (PV) systems through the implementation of a Maximum Power Point Tracking (MPPT) controller, which incorporates an incremental conductance (NNIC) approach alongside fuzzy logic and neural network incrementality. The system comprises of a Zeta DC-DC converter for output voltage control after an inverter and filter unit meant to guarantee a continuous power supply to a three-phase grid. The main goal is to maximize energy extraction from the solar module under different climatic circumstances, thereby guaranteeing grid compatibility and therefore enhancing power quality. The suggested MPPT controller exhibits superior convergence speed and heightened precision in identifying the maximum power point, adeptly integrating the benefits of both Fuzzy Logic and Neural Network methodologies in contrast to conventional techniques. The Zeta converter has been chosen due to its ability to enhance and regulate the output from the photovoltaic system, ensuring consistent power supply to the inverter and thereby reducing ripple effects. Crucial for grid adherence, the inverter converts direct current into alternating current, while the filter unit ensures minimal harmonic distortion. The simulation results indicate that by optimizing power extraction, the system significantly reduces energy losses and enhances overall efficiency. Large-scale grid-connected photovoltaic systems would benefit from the integration of fuzzy/NN INC-based maximum power point tracking and Zeta converters, as this combination ensures remarkable flexibility and robustness. This arrangement greatly enhances the quality of the electricity, therefore promoting a more dependable and sustainable energy supply.