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Artificial Intelligence (AI) has become an increasingly relevant technological component in the digital transformation of ports, supporting improvements in operational efficiency, safety and environmental performance. In recent years, several literature reviews have examined AI applications in maritime and port-related contexts; however, these studies typically focus on specific dimensions, such as safety and risk management, smart port development, or individual Machine Learning techniques. Consequently, a consolidated and application-oriented overview explicitly centered on port management and operations remains limited. This study addresses this gap by conducting a systematic literature review aimed at synthesizing current scientific evidence on the application of AI in port management and operational activities, while identifying emerging research gaps. The review follows the PRISMA protocol and analyses 30 peer-reviewed journal articles published between 2021 and early 2025. The results indicate that AI is predominantly applied in operational areas such as vessel arrival forecasting and scheduling, berth and quay crane allocation, container handling and predictive maintenance. Machine Learning and Deep Learning approaches are the most frequently adopted and exhibit higher levels of empirical validation. Reported outcomes include improvements in operational efficiency, enhanced decision-support accuracy and reductions in environmental impact. Despite these advances, the literature highlights persistent challenges related to data availability and quality, system interoperability, cybersecurity and the adaptation of AI solutions to heterogeneous port environments. The study concludes that, while AI adoption in ports is progressing beyond experimental stages, further empirical research and structured implementation strategies are required to support scalable and secure deployment across different port contexts.