Real-Time Flight Path Optimization: Enhancing Efficiency in Modern Aviation

Modern air traffic systems need intelligent solutions to manage flight efficiency and minimize delays because their complexity continues to increase. A real-time flight path optimization framework uses machine learning and deep reinforcement learning methods to handle the unanticipated behaviors of airspace dynamics. Our approach uses real-time aviation data analysis to predict potential delays before modifying flight paths in order to decrease both travel duration and fuel use. The system uses Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) algorithms to create adaptive routing decisions that suit changing situations. The evaluation process uses simulated air traffic control (ATC) environments which imitate actual conditions to conduct comprehensive tests on decision-making performance throughout difficult situations. The system uses a userfriendly interface built with Tkinter and Matplotlib to present routing outcomes and performance metrics in a clear manner to its users. Simulation data demonstrates significant improvements in route efficiency alongside decreased delay risks while achieving substantial fuel cost savings. The current research links innovative AI solutions with operational aviation tools and establishes a foundation for upcoming studies on deploying real-world applications along with advanced learning algorithms to assist flight management systems.