<![CDATA[Disaster prediction and analysis are crucial components in mitigating the impacts of natural hazards such as floods, earthquakes, and landslides. Conventional systems often rely on deterministic models and limited historical data, which restrict their accuracy and adaptability to dynamic environmental changes. The emergence of Generative Artificial Intelligence (GenAI), particularly models based on deep learning and generative architectures such as Generative Adversarial Networks (GANs) and Diffusion Models, introduces new opportunities for synthetic data generation and predictive simulation. This study aims to explore the implementation of GenAI in disaster prediction and analysis by reviewing recent literature and practical applications in Indonesia. The proposed framework integrates multimodal data—including meteorological, seismic, and remote sensing data—into generative models to simulate disaster scenarios and improve early warning systems. The results indicate that GenAI can enhance data diversity, reduce bias in model training, and support real-time decision-making in disaster management. The study concludes that GenAI has strong potential to revolutionize disaster analytics and strengthen climate resilience through adaptive, data-driven insights. Thus, the output of this research is conceptual and focuses on designing a framework, while empirical testing forms the basis for further research development.]]>
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