The integration of photovoltaic (PV) devices with grid infrastructure is critical for generating sustainable energy. However, the ongoing difficulty of islanding poses a serious threat to the grid’s stability and safety. This work offers an AI-based anti-islanding technique that will improve the reliability and efficacy of grid-connected PV systems. There have been a huge number of research and developments in the field of anti-islanding techniques. The study begins with a thorough literature review of existing anti-islanding approaches and difficulties connected with modern PV installations. This paper demonstrates the use of quadratic SVM classifier for islanding detection. The suggested anti-islanding solution uses real-time monitoring of grid characteristics and sophisticated decision-making algorithm to differentiate between regular grid operations and potential islanding circumstances. For training the classifier, diverse cases have been accounted. The proposed scheme has high accuracy along with fast and economic responses. Simulation results show that the proposed strategy effectively mitigates islanding risks while decreasing false positives, hence maintaining grid stability.