Objective: The Guangdong–Hong Kong–Macao Greater Bay Area is geographically situated in a region frequently impacted by severe tropical cyclones, which pose persistent threats to infrastructure resilience and public safety. Among critical facilities, airport terminals—especially those located along coastal zones with high typhoon landfall probability—face significant challenges regarding structural integrity under extreme wind events. Wind uplift forces during typhoons can cause catastrophic damage to roofing systems, cladding elements, and even primary structural components if not adequately mitigated. Studies have highlighted the necessity of proactive monitoring mechanisms that capture real-time structural behavior under extreme wind loads. Nonetheless, integrated solutions tailored specifically for large-span airport terminals remain limited. This study addresses this gap by proposing a comprehensive structural safety monitoring and early warning system explicitly designed for coastal airport environments, with a focus on improving preparedness for wind disasters, enhancing structural performance, and minimizing operational downtime during extreme weather events. Methods: The study is grounded in the engineering context of Zhuhai Airport Terminal 2, a representative large-span spatial steel structure exposed to frequent typhoons. A multi-layered structural health monitoring framework was developed, comprising more than 250 sensor units across 14 distinct categories, strategically deployed to capture critical environmental and structural parameters. The sensing system includes three-dimensional anemometers for wind speed and direction measurements, differential pressure sensors for surface wind pressure, strain gauges and fiber-optic sensors for stress–strain monitoring, and accelerometers for dynamic response characterization. An advanced data acquisition and processing platform integrates safety assessment algorithms with intelligent alarm logic, enabling continuous evaluation of structural conditions. Notably, the system employs enhanced fiber-optic intelligent sensing reinforcements directly bonded to continuously welded roof panels, providing high-resolution strain measurements while improving resistance to wind uplift. Multiple sensor types are co-located at identical monitoring points to enable multi-source heterogeneous data fusion, thereby enhancing redundancy, accuracy, and robustness in data interpretation. Results: The proposed system was empirically validated during Typhoon Wipha in 2025, which made landfall near the Pearl River estuary with sustained winds exceeding design-level thresholds. The monitoring system successfully recorded detailed time histories of wind velocity, gust factors, and localized pressure peaks across different roof zones. The data indicate that maximum wind pressures occurred at roof corners and leading edges, consistent with aerodynamic theory, although their magnitudes slightly exceeded initial design assumptions. Strain measurements further show that the proposed fiber-optic reinforcement effectively reduced peak tensile stresses in roof panels, confirming its dual function in structural health monitoring and mechanical enhancement. Multi-source heterogeneous data fusion enabled precise correlation between wind field characteristics and structural dynamic responses, allowing the identification of potential overstress conditions before they reached critical levels. Within seconds of their detection, anomalous trends triggered intelligent alarms, demonstrating the system's responsiveness and reliability. Comparative analysis against conventional single-source monitoring approaches showed a marked improvement in detection accuracy and situational awareness. Conclusions: This study demonstrates that an integrated, sensor-rich monitoring and early warning system can significantly improve the resilience of coastal airport terminals to typhoon-induced wind hazards. By combining advanced sensing technologies, intelligent data fusion, and targeted structural reinforcement, the proposed framework not only delivers real-time safety assurance but also generates valuable datasets for design code and maintenance strategy refinement. The approach is readily adaptable to other large-span spatial structures in hurricane-prone regions, offering broad applicability in civil infrastructure protection. Future work may explore integration with predictive modeling tools, machine learning-based anomaly detection, and automated mitigation measures to further strengthen disaster preparedness.