<p>This paper presents a novel hybrid machine learning method for enhanced islanding detection in distributed generation systems. The proposed approach integrates distinct feature extraction from voltage and current signals at the point of common coupling with an optimized Extreme Gradient Boosting (XGBoost) classifier to accurately differentiate islanding events from normal grid disturbances. Validated on a public residential microgrid dataset, the method demonstrates superior performance by achieving a high detection accuracy of 97.83%, effectively eliminating the non-detection zone, and maintaining a detection time of under two cycles. This approach provides a robust, non-intrusive, and computationally efficient solution for anti-islanding protection, significantly outperforming conventional passive techniques, while the use of a publicly available dataset ensures full reproducibility and offers a benchmark for future research.</p>