Semiconductor ALD (Atomic Layer Deposition) is a precision-critical process involving sequential stages and large-scale time-series data from recipe settings and sensors. This paper proposes an edge AI architecture combining lightweight models on edge devices with centralized model training. The system enables early predictions from recipe data, real-time adjustments via sensor inputs, and continuous refinement using post-process outcomes. Only extracted features and result data (film thickness and uniformity) are transmitted to reduce communication overhead and protect sensitive data. The architecture supports performance monitoring and seamless model redeployment, adapting to changing equipment and environments. This approach improves product quality, reduces defect rates, and enhances manufacturing adaptability.