<![CDATA[This study aims to analyze the integration of big data and artificial intelligence in local government disaster risk management to support more responsive and adaptive governance. The research focuses on the use of big data and AI for disaster mitigation, early warning, emergency response, and post-disaster aid distribution. This study employs a qualitative method with an exploratory-descriptive approach and conceptual model development. Data were collected from secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, disaster management guidelines, institutional reports, and regulatory materials related to digital governance, disaster risk reduction, big data analytics, artificial intelligence, and local government management. The data were analyzed using thematic analysis by classifying findings into key themes: data integration, AI-supported risk prediction, early warning, emergency coordination, aid distribution, institutional readiness, ethical risks, and public accountability. The findings show that big data can improve disaster governance by integrating geospatial, meteorological, population, infrastructure, social media, public complaint, and social assistance data. AI strengthens this process through predictive analytics, damage estimation, urgent-needs classification, evacuation support, misinformation detection, and assistance prioritization. The study contributes by proposing an integrated big data and AI-based local government disaster risk management model that links digital technology with mitigation, early detection, emergency response, and post-disaster recovery. The study implies that local governments must strengthen data governance, inter-agency coordination, human-resource capacity, transparency, privacy protection, and human oversight to ensure that AI-based disaster management remains accountable, ethical, and oriented toward public safety.]]>
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