<![CDATA[This study aims to formulate a Big Data and Artificial Intelligence (AI)-based model for monitoring the SDGs within a data-driven governance framework. Using a descriptive qualitative approach and a conceptual study, the study focuses on four aspects: current monitoring mechanisms, potential Big Data-based data sources, opportunities for AI utilization, and data governance and inter-agency interoperability. Data were obtained from literature reviews, policy documents, and news sources on the implementation of the SDGs, Big Data, AI, and data governance. The results show that the weakness of SDGs monitoring lies not in the availability of indicators or data, but rather in the monitoring and governance architecture, which remains administrative, periodic, and sectoral, resulting in slow information flow and minimal analytics. Abundant data sources actually meet the characteristics of Big Data, and AI can transform indicator readings into real-time, analytical, and predictive insights. However, their utilization has not been optimal due to interoperability constraints and procedural data governance. Monitoring transformation requires the integration of Big Data, the use of AI, and a shift in governance towards interoperability across agencies. Further research suggestions include the empirical testing of the model through case studies in local governments/agencies, the development of a Big Data and AI-based monitoring dashboard prototype, and the measurement of its impact on the quality of policy-making]]>
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