Facility management teams handle numerous daily tasks, making efficient scheduling crucial. Missed or delayed tasks can disrupt operations, affecting efficiency and resource allocation. This project aims to develop a real-time, interactive dashboard that predicts task misses, monitors completion patterns, and optimizes scheduling. Using historical data and machine learning models like XGBoost, the system will analyze task frequency, workload distribution, and seasonal patterns. The dashboard will feature heatmaps, trend graphs, and risk indicators, ensuring usability, scalability, and real-time updates to enhance decision-making and resource management.