Asset Management (AM) is quickly transforming due to the digital revolution induced by Industry 4.0, in which Cyber–Physical Systems (CPS) and Digital Twins (DT) are taking key positions in monitoring and optimizing physical assets. With more intelligent functionalities arising in industrial contexts, Machine Learning (ML) has transitioned from playing a supporting role to becoming a core constituent of asset operation. However, while the Asset Administration Shell (AAS) has become an industry standard format for digital asset representation, incorporating ML models into this format is a significant challenge. In this research, a control valve, a common asset in industrial equipment, is used to explore the modeling of a machine learning model as an AAS submodel, including its related elements, such as parameters, hyperparameters, and metadata, in accordance with the latest guidelines issued by the Industrial Digital Twin Association (IDTA) in early 2025. The main contribution of this work is to clarify basic machine learning principles while demonstrating their alignment with the AAS framework, hence facilitating the further development of smart and interoperable DTs in modern industrial environments.