Digital Twin (DT) technology has developed from a primarily representational idea into a manufacturingoriented digital system that can connect physical equipment, production data, models, analytics and decision services. The reviewed work shows a clear progression: early studies concentrated on distinguishing Digital Models, Digital Shadows and Digital Twins, while later work increasingly addresses practical deployment, real-time data integration, simulation, artificial intelligence, robotics, predictive maintenance, edge computing, federated learning, generative AI and automated decision support. The papers also reveal that the central difficulty is no longer only creating a virtual representation; it is maintaining a reliable connection between heterogeneous physical systems and useful digital services. Across the papers, sensors and industrial communication provide the data foundation, simulation supplies virtual experimentation, AI supplies prediction or optimization, and increasingly intelligent architectures support autonomous or humanguided decisions. The survey compares the methods, technologies, applications and reported outcomes and identifies recurring gaps in scalability, interoperability, model fidelity, latency, data quality, security and explainability.

Nasrin TM, Ms. Riya K Prakash, Dr. L. C. Manikandan, "Digital Twin in Manufacturing" International Journal of Scientific Research and Engineering Development, V9(5): Page(478-485) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.