Illegal drone intrusion near airport runways poses a critical threat to civil aviation safety, creating an urgent need for runway-side vision systems that can detect intruding UAVs early enough for safety warning and collision-risk mitigation. However, the development of such detectors is severely hindered by the scarcity of annotated real-world data in this high-security scenario. To address this bottleneck, we present SynthAirDrone, the first high-fidelity synthetic dataset for UAV intrusion detection in airport runway environments, together with an intelligent data generation framework integrating scene-aware placement and multi-criteria quality assessment. The proposed method uses sky-region segmentation to guide physically plausible drone placement, and combines perspective-aware scaling, Poisson image editing, and a four-dimensional quality scoring system—covering sky overlap, lighting consistency, size plausibility, and edge continuity—to improve visual plausibility and semantic consistency. The resulting dataset comprises 6500 high-quality images, all annotated in YOLO-compatible format. Using the lightweight YOLOv11n model, we show that models trained solely on SynthAirDrone exhibit non-trivial cross-domain transfer to Anti-UAV, while mixed training with limited real data provides the strongest real-world performance under the present setting. Ablation studies further confirm that a quality threshold of $\tau =0.6$ achieves the best trade-off between diversity and fidelity. Overall, this work delivers a reproducible and efficient synthetic data solution for UAV detector development in high-security, data-scarce airport-runway scenarios.