In this study, the challenges of flight route prediction and flight delay prediction are addressed as critical problems in the aviation industry. Flight route prediction plays a vital role in ensuring airspace safety and achieving fuel efficiency, while flight delay prediction is essential for improving operational efficiency and passenger satisfaction. This study highlights that analyzing ADS-B data with deep learning methods provides effective solutions to operational challenges in the aviation industry. By leveraging the advanced capabilities of CNN and LSTM architectures, this research showcases how modern predictive models outperform traditional methods in terms of scalability, accuracy, and adaptability.