This study uses a national level case study of Greek SSS to identify structural, data-related, and governance limitations that impede evidence-based policy design. Key performance indicators (KPIs) and composite indices (CIs) are developed to assess connectivity, accessibility, and operational efficiency across the island and between the islands and the mainland. These empirical findings reveal fragmented data, heterogenous service patterns, and gaps in current governance frameworks, highlighting challenges that extend to regional and international coordination.
Building on these insights, the paper proposes a conceptual AI framework to address the identified limitations. Machine learning can forecast SSS performance trands, while natural language processing can harmonize policy documents across jurisdictions. By linking empirical limitations with this forward-looking conceptual approach, the study demonstrates how AI can transform fragmented maritime data into interoperable, collaborative governance mechanisms that enhance MSP implementation and cross-border cooperation.