As urbanization accelerates, aging infrastructure demands more advanced inspection methods for structural health monitoring. The growing integration of artificial intelligence (AI) and computer vision technologies has significantly enhanced damage detection accuracy while simultaneously reducing inspection time and operational costs. Despite these advantages, the adoption of AI-based technologies in infrastructure maintenance remains limited due to challenges related to data. One major issue is the lack of comprehensive, task-specific annotated datasets. Another is the poor quality of images captured by drones or mobile devices, which are often affected by noise, blurring, and inconsistent lighting. Although recent advances in generative AI offer promising support for structural health monitoring, it remains unclear which models are best suited for specific tasks. This study examines the use of generative AI in structural health monitoring, focusing on key challenges such as limited datasets and low-quality image restoration. The review covers a range of generative AI technologies, outlining their principles, strengths, limitations, and representative applications to support the selection of appropriate tools for specific tasks. Generative AI models enable accurate image segmentation and structural anomaly detection using limited training data. The paper also explores new opportunities for integrating multi-modal generative AI to enhance human–computer interaction in support of structural health monitoring. A framework is proposed to streamline the use of generative AI technologies for data augmentation, image restoration, damage inspection, and human–computer interaction in structural health monitoring.