Emerging Trends in AI and Machine Learning Applications in Maritime Power Systems: A Bibliographic Analysis of IEEE TIA

The maritime industry is a major contributor to global greenhouse gas emissions, necessitating sustainable energy solutions to meet the IMO 2050 decarbonization targets. Artificial intelligence (AI) and machine learning (ML) have emerged as enablers for optimizing fuel consumption, enhancing energy management, and advancing smart-port operations. This paper presents a bibliographic analysis of AI and ML applications in maritime power systems, focusing on IEEE Transactions on Industry Applications (TIA) publications from 1990 to 2025. Our paper identifies key themes, including fuel optimization, autonomous vessel operations, and renewable energy integration, highlighting the role of AI in advancing maritime technologies. A comparative evaluation with other leading IEEE journals such as IEEE Transactions on Sustainable Energy (TSTE), IEEE Transactions on Power Systems (TPS), and IEEE Transactions on Smart Grid (TSG), highlights TIA's unique, particularly in AI-driven maritime research. Moreover, this paper also identifies research gaps and suggests future directions that emphasize the need for detailed case studies, specific parameter settings, and simulation results to validate operational improvements.