<p>In the field of industrial thermal equipment, numerous theoretical concepts and frameworks exist within the literature. However, the practical implementation of these frameworks remains limited, particularly in validating their efficiency on a large scale. This paper aims to bridge the gap between theory and practice by implementing a framework to demonstrate its real-world applicability. It addresses problems in adaptation of Digital Twin in industrial domain. Specifically, it seeks to assess the effectiveness of key technical parameters and investigate the utility of Digital Twin technology for enhancing thermal equipment operations. The research methodology considers development of code to collect sensor data, route it on various cloud modules to exercise Digital Twin securely by experimentation using a small-scale model for validation purposes. It’s important to note that the study presented in this paper is confined to focus on typical sensors commonly used in industrial settings. Through the experimentation process, it is revealed that the implemented framework proves to be suitable and effective for industrial thermal equipment. Real-time processing of key sensor data enables visualization for identifying component status. The study concludes that large-scale applications utilizing IoT and Digital Twin frameworks are not only feasible but also can address industrial concerns beyond the capabilities of traditional PLC/DCS systems. Industrial plants will have impact on seamless adoption of IoT and Digital Twin frameworks that encourages integration with other emerging technologies such as artificial intelligence (AI) and machine learning (ML), leading to further advancements in thermal equipment management. By optimizing thermal equipment operations through real-time monitoring and predictive maintenance, and positive environmental impact by significantly improved energy efficiency, resulting in reduced consumption and greenhouse gas emissions.</p>