Human–machine interfaces (HMI) facilitate communication between humans and machines, and their importance has increased in modern technology. However, traditional HMIs are often static and do not adapt to individual user preferences or behavior. Adaptive User Interfaces (AUIs) have become increasingly important in providing personalized user experiences. Machine-learning techniques have gained traction in User Experience (UX) research to provide smart adaptations that can reduce user cognitive load. This chapter focuses on the development of adaptive HMIs within industrial contexts, offering a structured framework. It provides an overview of the past and present of HMIs and AUIs, while also outlining prospects for future research. The framework, enhanced by user interactions and Context-Aware Recommendation Systems (CARS), aims to provide tailored adaptations, thereby improving overall UX. A case study highlights real-time remote monitoring in smart factories, improving the ease of use and ease of decision making. The study demonstrates the framework usage in addressing real-world HMI, discussing results, challenges, and limitations.