Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Balaji, K. Title A name given to the resource Integrating Artificial Intelligence in the Blue Economy: A Case-Based Study of Smart Marine Ecosystems for Climate Change Mitigation and Circular Economy Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AI and Climate-Smart Policies for Blue Economy: Eco-Digital Futures;pp.153-179 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-5238-1.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-5238-1.ch007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033898871?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033898871?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Balaji K., Christ University, Bengaluru, India Description An account of the resource The Blue Economy represents a critical pathway toward sustainable development, balancing economic growth with the conservation of marine ecosystems. This chapter explores the transformative role of Artificial Intelligence (AI) in advancing sustainable practices within the Blue Economy through a series of global case studies. It examines how leading companies and initiativessuch as Sinay, Saildrone, Sea.AI, Buffalo Automation, IBM, Microsoft, Ocean Cleanup, and Veolia Groupleverage AI-driven solutions for real-time environmental monitoring, predictive climate modeling, autonomous navigation, and circular waste management. Key insights reveal that AI enables data-driven decision-making, reduces carbon emissions, enhances operational efficiency, and supports closed-loop recycling systems, while confronting challenges related to data integration, regulatory frameworks, and economic viability. Policy implications highlight the urgent need for adaptive regulations, open data ecosystems, and public-private partnerships to scale smart marine ecosystems globally. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. Publisher An entity responsible for making the resource available IGI Global Scientific Publishing Relation A related resource ISBN: 979-833735240-4; 979-833735238-1; 979-833735239-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online