The article explores the potential applications of artificial intelligence (AI) in maritime fleet management, focusing on improving operational efficiency and reducing costs. An analysis of key technological solutions is presented, including predictive maintenance, intelligent routing systems, crew performance monitoring tools, and energy consumption optimization. It is demonstrated that machine learning algorithms processing vast datasets, such as Automatic Identification System (AIS) data, weather information, and vessel sensor readings, can predict emergency situations and schedule maintenance based on actual equipment wear.

The study examines case studies from Maersk, Shell, Wärtsilä, and other companies, highlighting fuel savings of up to 15%, reductions in unplanned maintenance events, and improvements in environmental sustainability. Special attention is given to decision-support systems that integrate diverse data sources into a unified information platform, enabling comprehensive analysis and timely decision-making.

The implementation of AI technologies can enhance not only safety levels but also the profitability of maritime transport by optimizing cargo flows and reducing fuel and maintenance costs. The article concludes with practical recommendations for shipping operators transitioning to a "digital" fleet and outlines promising directions for further research. The information presented will be of interest to professionals and researchers in maritime logistics, digital transformation, and operational management who aim to integrate advanced AI-driven models with systems analysis to develop innovative strategies for improving efficiency and reducing costs in maritime fleet management amid global industry dynamics.

artificial intelligence, maritime fleet management, predictive maintenance, route optimization, cost reduction

Stanković J. J. et al. Social, economic and environmental sustainability of port regions: Mcdm approach in composite index creation //Journal of Marine Science and Engineering. – 2021. – Vol. 9 (1). – pp. 74.

Mollaoglu M., Altay B. C., Balin A. Bibliometric review of route optimization in maritime transportation: Environmental sustainability and operational efficiency //Transportation Research Record. – 2023. – Vol. 2677 (6). – pp. 879-890.

Tu E. et al. Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology //IEEE Transactions on Intelligent Transportation Systems. – 2017. – Vol. 19 (5). – pp. 1559-1582.

Chen X., Ma D., Liu R. W. Application of Artificial Intelligence in Maritime Transportation //Journal of Marine Science and Engineering. – 2024. – Vol. 12 (3). – pp. 439.

Atik O., Arslan O. Use of eye tracking for assessment of electronic navigation competency in maritime training //Journal of eye movement research. – 2019. – Vol. 12 (3). – pp. 7-15.

Chen J. et al. Identifying factors influencing total-loss marine accidents in the world: Analysis and evaluation based on ship types and sea regions //Ocean Engineering. – 2019. – Vol. 191. – pp. 1-12.

Fan A. et al. Joint optimisation for improving ship energy efficiency considering speed and trim control //Transportation Research Part D: Transport and Environment. – 2022. – Vol. 113. – pp. 5-11.

Kuroda M., Sugimoto Y. Evaluation of ship performance in terms of shipping route and weather condition //Ocean Engineering. – 2022. – Vol. 254. – pp. 1-10.

Pallotta G., Vespe M., Bryan K. Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction //Entropy. – 2013. – Vol. 15 (6). – pp. 2218-2245.

Susto G. A. et al. Machine learning for predictive maintenance: A multiple classifier approach //IEEE transactions on industrial informatics. – 2014. – Vol. 11. (3). – pp. 812-820.

Wahl A. M., Kongsvik T. Crew resource management training in the maritime industry: a literature review //WMU Journal of Maritime Affairs. – 2018. – Vol. 17 (3). – pp. 377-396.

Yang Z., Yang Z., Yin J. Realising advanced risk-based port state control inspection using data-driven Bayesian networks //Transportation Research Part A: Policy and Practice. – 2018. – Vol. 110. – pp. 38-56.

Zis T. P. V., Psaraftis H. N., Ding L. Ship weather routing: A taxonomy and survey //Ocean Engineering. – 2020. – Vol. 213. – pp. 1-14.

Durlik I. et al. Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications //Applied Sciences. – 2024. – Vol. 14 (18). – pp. 8420.