This paper presents the design and implementation of a system for processing and analyzing large-scale time-series data generated in semiconductor deposition processes. By adopting a real-time data collection and analysis architecture divided into Edge and Server layers, the system enables continuous retraining and updating of machine learning models based on real-time data streams. The evaluation of the model's performance demonstrates that additional training data significantly improves the model's accuracy in predicting process outcomes. Our approach not only provides a practical solution for real-time decision-making support in semiconductor manufacturing but also offers a scalable and adaptable framework applicable to various industrial sectors requiring real-time data analysis and processing. The results highlight the potential of integrating big data and artificial intelligence technologies to drive industrial innovation and optimize manufacturing processes.