<p>Industrial data processing refers to the collecting, storage, and analysis of data for the linked enterprise. Effective data management boosts the efficiency of industrial businesses. Modern industrial firms create a significant volume of data, which overwhelms and inefficiently uses existing technologies. Previous data management techniques impede the working process of smart manufacturing. The data generated by manufacturing facilities grows quickly and must be processed, categorized, and saved in the proper sequence. All of this requires time and human work. In addition, on the Internet of Things (IoT), all parts of the network are connected in the same way, resulting in a common image structure. Ironically, these integrated IoT devices have low battery life, are time-consuming, and have long-lasting performance. To be effective in such work, these tools must solve many problems, including the use of sustainable practices, improved management, and facilitate rapid data communication. In a manufacturing environment, these devices serve as data sources, providing a wealth of information for production operations. Nonetheless, the enormous, varied, and time-sensitive nature of the data presents significant obstacles to real-time gathering, processing, and decision-making. As smart manufacturing is increasingly used in digital computing, IoT will become an integral part of the industrial data management they came. Moreover, due to their low power consumption and long lifespan, they are very easy to develop using quantum-dot cellular automata (QCA) technology. The benefits of QCA technology not only apply to small IoT systems, but also focus on optimizing space utilization, energy efficiency, and performance. The performance and effectiveness of the model have been rigorously tested using tools such as QCADesigner-E 2.2 and QCAPro.</p>