This work presents an intelligent Photovoltaic (PV) grid integration system employing a Northern Goshawk Optimization Algorithm (NGOA)-based Radial Basis Function Neural Network (RBFNN) for Maximum Power Point Tracking (MPPT) and Soft Clamp X-Gain Boost (SC-XGB) converter for enhanced voltage regulation. The proposed system aims to optimize energy harvesting from PV source and ensure stable power delivery to a three-phase grid. The RBFNN is trained offline using comprehensive PV datasets and directly predicts the optimal duty cycle from measurable PV inputs during real-time operation, eliminating the need for auxiliary MPP pre-estimation algorithms, while NGOA enhances RBFNN’s learning capability by fine-tuning its weights and biases for rapid and accurate MPPT performance even under varying irradiance and temperature conditions. PV output is connected to SC-XGB, which efficiently raises the Direct Current (DC) voltage and is thus controlled by Pulse Width Modulation (PWM) signals which are generated according to MPPT output. Regulated DC output is then transformed into a three-phase Alternating Current (AC) by a Voltage Source Inverter (VSI), the output of which is taken through an LC filter to reduce harmonics before the power is fed into grid. Simulation is done in MATLAB showing the capacity of NGOA-RBFNN to track Maximum Power Point (MPP) at very high speed and accuracy. The system achieves superior voltage regulation of 95.24% efficiency, reduced Total Harmonic Distortion (THD) and enhanced dynamic performance over traditional MPPT control methods.