Ensuring the quality and productivity of sinter production in the iron industry relies heavily on monitoring and maintaining the Burn-Through Temperature (BTP) position and temperature. However, critical data such as %coke, coke fines, and sinter bed were previously unavailable, posing challenges to effective monitoring and prediction. This initiative focuses on deploying a real-time BTP prediction model in the sinter plant, leveraging advanced analytics and data integration techniques to overcome data limitations. By harnessing real-time data and predictive modeling, operators can optimize BTP monitoring, enhance process control, and improve overall sinter quality and productivity.