Integrated Energy System (IES), which covers electricity/gas/heat and other different energy sources, is an integral source of energy and Fault Detection in dynamic processing. Some key Challenges, such as collaborative planning, tracking optimization, threat review, state assessment, situational awareness, and general demand-side management, are addressed and discussed in this paper. Further, the Integrated Energy System using Machine Learning Technology (IES-ML) has a significant practical and strategic significance for related study and practice in China's energy system development proposed in this research. The Regional Internet Research (RIR) and Development In Energy (DIE) focus on fault detection in china's Energy-based district heating system. In comparison to the conventional power delivery system, IES-ML is used to enhance the economy efficiently. Besides, the protection, reliability, stability, and strength of multi-energy coupling have been validated. RIR and AIE are often used to minimize environmental demand from the District heating energy system. The experimental result shows that IES-ML achieves the highest accuracy of 98.67% and performance in fault detection and control in IES.