This paper explores the integration of machine learning (ML) technologies into museum installations. It examines seminal experiments in this domain, detailing the methods and algorithms utilized, and providing a comparative analysis to identify key trends and techniques in the field. The inquiry extends to the technical framework and artistic considerations involved in creating an emotion-reactive installation, highlighting the challenges associated with real-time individual’s analysis and the curation of responsive art content. Also the paper explores a case study focused on creating a video installation that reacts to an individual emotional state. By employing facial recognition cameras, this installation captures the emotional states of its viewers. These expressions are processed using ML algorithms to interpret emotional cues. Based on this analysis, the system selects and displays a video from a pre-set collection that corresponds to the viewer’s current mood, thereby enhancing the immersive experience of the artwork.