The drying process of transformer components is a critical step in manufacturing, ensuring the integrity of insulation and the long-term reliability of the products. However, traditional drying methods are energy-intensive, resulting in high electricity consumption and operational costs. This study explores an AI-driven smart drying solution that utilizes machine learning models to accurately predict the drying state of transformer components. The proposed system optimizes energy use while maintaining quality standards by analyzing real-time sensor data, environmental conditions, and historical drying patterns. Our approach involves developing and training machine learning models, including regression and classification techniques, to estimate moisture content and determine optimal drying times. The predictive capabilities of these models allow for dynamic adjustments to the drying parameters, reducing unnecessary energy consumption and minimizing production costs. Additionally, the system includes real-time monitoring and feedback mechanisms that enable adaptive control based on the drying state of the transformer components. The results demonstrate significant improvements in energy efficiency, as optimized electricity demand leads to cost savings without compromising process effectiveness. This AI-driven methodology offers a scalable and adaptable framework for industrial drying applications, aligning with the broader objectives of digital transformation in production systems. This case study highlights the potential of AI and machine learning in enhancing industrial efficiency, paving the way for smarter and more sustainable manufacturing processes.