<![CDATA[Illegal, Unreported, and Unregulated (IUU) Fishing remains a persistent challenge for Indonesia because vast archipelagic waters, heterogeneous vessel activity, and fragmented institutional mandates complicate continuous monitoring and timely enforcement. This study develops an integrated conceptual framework that connects digital maritime surveillance, multisource data fusion, artificial intelligence, maritime intelligence, Maritime Domain Awareness, and law enforcement. A structured integrative literature review was conducted using 30 peer-reviewed sources published between 2017 and 2024, selected for direct relevance to IUU Fishing, AIS/VMS analytics, satellite and radar surveillance, anomaly detection, maritime data fusion, institutional governance, and legal enforcement. The synthesis shows that cooperative systems such as AIS and VMS provide continuity but can be disabled or manipulated, whereas radar, SAR, optical imagery, and VIIRS supply non-cooperative detection but face temporal, spatial, cost, and processing constraints. Their value increases when heterogeneous observations are fused, quality-controlled, and translated through AI-supported anomaly detection into prioritized maritime intelligence. The framework therefore treats technology as only one layer of a broader enforcement system in which interoperable data, human analytical capacity, institutional coordination, digital-evidence governance, and legal readiness determine whether detection becomes actionable enforcement. The study contributes four testable propositions linking multisource integration, analytical quality, Maritime Domain Awareness, and enforcement responsiveness. For Indonesia, the proposed architecture emphasizes coordinated data sharing among maritime agencies, risk-based targeting, transparent digital-evidence handling, and empirical validation in IUU Fishing hotspots.]]>
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