This research proposes an innovative grid-connected photovoltaic (PV) system that incorporates both neural network and fuzzy logic techniques in a combined optimal control strategy. The system aims to optimize the performance and efficiency of PV installations while eliminating the need for a transformer. By utilizing neural networks, the system can learn and adapt to real-time data, enabling accurate predictions and efficient control of the PV system. The fuzzy logic component adds a human-like decision-making capability, allowing the system to handle uncertainties and variations in environmental conditions. The combined approach offers improved power generation and grid synchronization, along with enhanced power quality and stability. This novel PV system provides a cost-effective and intelligent solution for grid-connected PV installations, enabling efficient utilization of solar energy