Efficient baggage handling is essential for smooth airport operations, directly impacting passenger satisfaction, airline performance, and logistical efficiency. With rising air travel demand, especially in Japan where labor shortages and limited space are critical issues, conventional Baggage Handling Systems (BHS) face increasing strain. This research proposes a compact Early Baggage Storage (EBS) system to address these challenges by optimizing baggage flow and minimizing delays. Unlike fixed-constraint models that assume uniform baggage arrival, the proposed simulation-based approach accounts for real-world fluctuations, such as passenger arrival patterns, enabling dynamic system responses. The study comprises three stages: model construction, simulation using AnyLogic (2024), and performance evaluation. The model replicates baggage flow from check-in to final loading, including sorting, screening via Explosive Detection System (EDS), and make-up area processing. Two EBS algorithms are analyzed: a conventional model and a proposed dynamic model that adjusts storage duration and baggage release intervals. The evaluation highlights the limitations of the conventional EBS, where smaller storage capacities cause congestion, and only larger capacities maintain smooth operations, though this comes with increased infrastructure costs. In contrast, the proposed EBS dynamically regulates baggage, improving subsystem balance and enabling smaller storage sizes to achieve comparable performance. This approach reduces congestion and infrastructure needs, making it a more efficient and cost-effective solution. Future work includes validating the proposed system in real airport settings and addressing make-up area delays through adaptive scheduling or buffer strategies to ensure broader applicability and operational robustness.