Microgrids are emerging as a crucial component in modern power grid architecture due to their ability to integrate renewable energy sources and enhance grid resilience. However, ensuring the reliable operation of microgrids remains a significant challenge, especially in detecting and mitigating faults promptly to prevent disruptions. This paper presents a novel approach for fault identification in microgrids using Hilbert Huang Transform (HHT) and Random Forest Tree (RFT). The features are extracted and processed using HHT from the faulted portion of distribution line and the same feature are supplied to RFT for identification of fault. The effectiveness of the proposed approach is demonstrated through simulation studies on standard microgrid model, showcasing its potential for enhancing the reliability and resilience of microgrid operations.