It employs machine learning models to predict passenger volumes, peak flows, and resource needs by analyzing bookings, schedules, weather, events, and historical patterns with 90%+ accuracy. Vital for airport automation, it prevents overcrowding, optimizes staffing/security lanes/gates, cuts wait times 20-30%, and maximizes capacity without infrastructure expansion amid 5% annual traffic growth. Implemented via cloud platforms integrating AODB, sensors, and APIs for real-time updates, automated alerts, and dynamic resourcing recommendations.