Operating under intricate and dynamic conditions, blast furnaces are susceptible to hanging events influenced by numerous parameters. However, the interior of a blast furnace is inaccessible and challenging to monitor directly, hindering the real-time detection of hanging incidents. This project focuses on developing predictive models to anticipate hanging events in blast furnaces. By leveraging advanced analytics and historical data, operators can forecast the likelihood of hanging incidents, enabling proactive interventions to mitigate risks and optimize furnace performance. Enhancing operational safety and efficiency, this initiative aims to safeguard personnel and equipment while maximizing production continuity in blast furnace operations.