<![CDATA[Regional food security remains a strategic challenge in Indonesia. This is evident in the need to ensure the availability of affordable animal protein by enhancing livestock productivity and farmers' capacity. This study aims to analyze the implementation of Artificial Intelligence (AI)-based Smart Livestock Farming, identify the program's internal and external factors, and formulate strategies to strengthen regional food security. A mixed-methods approach with a sequential exploratory design was employed, beginning with a qualitative phase followed by a quantitative phase. The research subjects were egg farmers participating in the program, while informants included training facilitators, field assistants, and policymakers. Data were collected through interviews, observations, documentation reviews, and Likert-scale questionnaires. Research instruments included interview guides, observation sheets, documentation checklists, and questionnaires. Data were analyzed qualitatively, followed by SWOT analysis and the Internal-External (IE) Matrix. The results indicate that the program integrates AI, training, and mentoring within a technology-based learning system. The SWOT analysis and IE Matrix positioned the program within a "hold-and-maintain" strategy. The study concludes that the sustainable implementation of AI requires enhancing farmers' capacity and securing institutional support. Consequently, continuous training and inter-institutional coordination must be strengthened to ensure the technology's sustainability and support regional food security.]]>
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