ABSTRACT Earthquake prediction and early warning systems play a central role in disaster risk reduction, and recent advancements in artificial intelligence, machine learning, and deep learning have expanded their analytical and operational potential. This study examines AI‐based earthquake prediction and early warning research through bibliometric analysis and BERTopic‐based topic modeling of 244 Web of Science‐indexed articles published between 2000 and 2025. The findings indicate a statistically significant increase in the number of publications after 2016, with deep learning and neural network architectures becoming especially prominent after 2020. The most highly cited studies focus on magnitude estimation and seismic signal classification, whereas probabilistic risk forecasting, seismic event detection, and public‐facing operational themes draw less attention. The studies concentrated primarily on algorithmic performance, while explainability, interoperability, data‐sharing conditions, and multi‐hazard integration are less visible. A targeted screening of public‐facing themes further indicates that explicitly social and behavioral concerns remain weakly represented and, where present, they cluster mainly around false‐alarm management and warning‐related operational issues. Overall, this study suggests that technical advancements have outpaced research on institutional capacity, coordination, implementation conditions, and public communication.
Sen et al. (Mon,) studied this question.
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