The rapid expansion of China’s civil aviation sector, particularly within the Beijing-Tianjin-Hebei airport cluster, has led to significant airspace congestion and operational inefficiencies. This study develops a dynamic flight slot allocation model that integrates both airport and airspace capacity constraints, accounting for real-time fluctuations in resource availability. The model aims to optimize slot distribution, minimize delays, and enhance operational efficiency by adapting to variations in airport and waypoint capacities, offering a more flexible solution compared with traditional static approaches. A case study based on real-world data from the Beijing-Tianjin-Hebei region demonstrates the model’s effectiveness. Computational experiments show that incorporating capacity fluctuations significantly reduces the need for slot adjustments, particularly at secondary airports with volatile demand. The results indicate a marked improvement in operational efficiency, including reduced adjustment times and fewer affected flights. This research underscores the value of adaptive data-driven strategies in managing complex air traffic systems and provides valuable insights for policymakers and aviation authorities. Future research could extend this work by incorporating additional dynamic factors, such as weather conditions and emerging technologies, to further enhance the sustainability and efficiency of air traffic management.