The Role of Artificial Intelligence in Sustainable Ocean Waste Tracking and Management: A Bibliometric Analysis

Mariam I. Adeoba (), Thanyani Pandelani, Harry Ngwangwa and Tracy Masebe
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Mariam I. Adeoba: UNISA Biomechanics Research Group, Department of Mechanical, Bioresources and Biomedical Engineering, College of Science, Engineering and Technology (CSET), University of South Africa, Florida (UNISA), Florida 1710, South Africa
Thanyani Pandelani: UNISA Biomechanics Research Group, Department of Mechanical, Bioresources and Biomedical Engineering, College of Science, Engineering and Technology (CSET), University of South Africa, Florida (UNISA), Florida 1710, South Africa
Harry Ngwangwa: UNISA Biomechanics Research Group, Department of Mechanical, Bioresources and Biomedical Engineering, College of Science, Engineering and Technology (CSET), University of South Africa, Florida (UNISA), Florida 1710, South Africa
Tracy Masebe: UNISA Biomechanics Research Group, Department of Mechanical, Bioresources and Biomedical Engineering, College of Science, Engineering and Technology (CSET), University of South Africa, Florida (UNISA), Florida 1710, South Africa

Sustainability, 2025, vol. 17, issue 9, 1-31

Abstract: The application of artificial intelligence (AI) in monitoring and managing ocean waste reveals considerable promise for improving sustainable strategies to combat marine pollution. This study performs a bibliometric analysis to examine research trends, knowledge frameworks, and future directions in AI-driven sustainable ocean waste management. This study delineates key research themes, prominent journals, influential authors, and leading nations contributing to the field by analysing scientific publications from major databases. Research from citation networks, keyword analysis, and co-authorship patterns highlights significant topics such as AI algorithms for waste detection, machine learning models for predictive mapping of pollution hotspots, and the application of autonomous drones and underwater robots in real-time waste management. The findings indicate a growing global focus on utilising AI to enhance environmental monitoring, optimise waste reduction methods, and support policy development for sustainable marine ecosystems. This bibliometric study provides a comprehensive analysis of the current knowledge landscape, identifies research gaps, and underscores the importance of AI as a crucial enabler for sustainable ocean waste management, offering vital insights for researchers, industry leaders, and environmental policymakers dedicated to preserving ocean health.

Keywords: marine pollution; artificial intelligence; waste monitoring; bibliometric; sustainable; trends (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
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