Smart manufacturing relies on real-time data integration to enhance efficiency, precision, and productivity in industrial operations. Networked metrology systems (NMS) play a crucial role in achieving this by ensuring accurate and reliable measurements across interconnected manufacturing processes. This paper explores the development of NMS to optimize smart manufacturing applications through real-time data integration. By leveraging advanced sensor networks, artificial intelligence (AI), and the Industrial Internet of Things (IIoT), NMS facilitate seamless data collection, analysis, and feedback, enabling adaptive manufacturing control. The study highlights the key components of NMS, including high-precision metrology instruments, wireless communication protocols, cloud-based data storage, and edge computing. These elements work in tandem to provide continuous monitoring, reducing measurement uncertainty and improving process stability. The integration of AI-driven analytics enhances predictive maintenance, process optimization, and decision-making by identifying deviations and ensuring corrective actions are taken in real time. Additionally, the paper examines various challenges in implementing NMS, such as data security, interoperability, and scalability. To address these concerns, the study proposes a robust framework incorporating blockchain for secure data transactions, standardized communication protocols for interoperability, and scalable architectures to accommodate evolving manufacturing demands. The findings indicate that adopting NMS significantly enhances manufacturing precision, reduces downtime, and lowers operational costs by minimizing errors and rework. The research further explores case studies where NMS have been successfully deployed in industries such as aerospace, automotive, and medical device manufacturing. These case studies demonstrate how real-time metrology integration leads to improved product quality, faster production cycles, and enhanced supply chain efficiency. Future research directions include the advancement of digital twin technology to create virtual models of manufacturing systems, enhancing predictive capabilities through AI-driven simulations. Furthermore, developing self-calibrating metrology instruments can further improve accuracy and reduce the need for manual intervention. In conclusion, networked metrology systems represent a transformative innovation in smart manufacturing by enabling real-time data integration, enhancing operational efficiency, and ensuring higher product quality. Their continued evolution, driven by AI, IIoT, and blockchain technologies, will shape the future of precision manufacturing.