Abstract: To address the prominent challenges in the Engineering Fluid Mechanics course for Marine Engineering majors, including abstract theoretical content, insufficient classroom interaction, disconnection between theoretical instruction and engineering practice, and simplistic assessment mechanisms, this study, guided by talent-cultivation requirements for marine engineering, integrates artificial intelligence (AI) technologies into the whole teaching workflow and establishes an integrated teaching-reform framework consisting of “development of online teaching resources — innovation of comprehensive teaching methodologies — reconstruction of multi-dimensional evaluation systems”. Dynamic visualization models are developed, tiered and categorized online question banks are built, and real-world marine-engineering case studies are introduced to leverage AI’s strengths in teaching-content generation, discipline-specific scenario adaptation and interactive feedback. These measures facilitate intuitive presentation of abstract theories, effective alignment between theoretical knowledge and engineering tasks, as well as on-demand delivery of teaching resources. Meanwhile, AI-enabled learning-status analysis, classroom interaction and diversified process-oriented assessments are implemented to form a closed-loop teaching mechanism covering pre-class, in-class and post-class stages. Course implementation results demonstrate that the proposed reform has been well received by students. The vast majority of students satisfy course assessment criteria, and the reform has shown positive effects. This research yields a lightweight, scenario-driven and readily implementable AI-assisted teaching pathway, which can offer practical references for intelligent teaching reform of Engineering Fluid Mechanics and other similar fundamental marine-related engineering courses.