The airline industry is characterized by fluctuating demand, intricate pricing, and inventory management. In that regard, this study is done regarding AI-driven revenue management systems, where many authors engage quite heavily with techniques of demand forecasting, pricing optimization, and inventory control. With Random Forest Regression, Catboost and LightGBM passenger demand can be predicted using fare class, lead time, and seasonality, optimizes seat allocation by fare category so as to maximize revenue. Evaluation is done using several datasets regarding booking patterns and market behavior in order to critically assess the accuracy, efficiency, and adaptability of the model. This study will demonstrate the strengths and trade-offs of AI techniques, indicating to the airline the power of using data for real-time decisions. Its strength lies in the improvement of demand forecasting combined with dynamic pricing and inventory management, maximized profitability, efficiency, and customer satisfaction.