Accurate prediction of main-engine rotational speed (RPM) is pivotal for energy-efficient ship operation and compliance with emerging carbon-intensity regulations. Existing approaches either rely on computationally intensive physics-based models or data-driven methods that neglect hydrodynamic constraints and suffer from label noise in mandatory reporting data. We propose a physics-informed LightGBM framework that fuses high-resolution AIS trajectories, meteorological re-analyses and EU MRV logs through a temporally anchored, multi-source alignment protocol. A dual LightGBM ensemble (L1/L2) predicts RPM under laden and ballast conditions. Validation on a Panamax tanker (366 days) yields ？1.52 rpm (？3%) error; ballast accuracy surpasses laden by 1.7%.
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