Airline performance and revenue are closely tied to operational reliability and proactive decision-making. Traditionalflight-delay management and fare adjustment systems often operate in isolation, leading to fragmented responses andsuboptimalfinancial outcomes. This paper presents FlightVision Pro, an intelligent framework that unifies flight-delay predictionanddynamic fare optimization through a hybrid ensemble learning approach. The model combines RandomForest, XGBoost, andNeural Network classifiers to forecast both delay occurrence and duration with high accuracy while maintaining lowinferencelatency. These predictions feed into a pricing optimizer that adjusts fares within a ±20 percent margin based on real-timedemandand operational risk. The framework is supported by a role-based, offline-capable dashboard built on lightweight componentssuchas SQLite and local model caching, ensuring 48-hour autonomy in limited-connectivity environments. Performanceevaluationdemonstrates improved predictive reliability (≥85 percent accuracy, MAE ≤ 15 minutes) and revenue gains of 5–15percentcompared with baseline systems. FlightVision Pro thereby establishes a unified operational intelligence layer connectingairlinecontrol centers, revenue management teams, and airport coordinators through a single, explainable, and resilient analyticsplatform. Keywords: Flight Delay Prediction, Ensemble Learning, Dynamic Fare Optimization, Revenue Management, XGBoost, RandomForest, Neural Network, Aviation Analytics, Offline Architecture, SQLite, Operational Intelligence, Real-Time Dashboard.