The prediction of the Estimated Time of Arrival (ETA) in maritime traffic plays a crucial role in enhancing voyage planning, logistics efficiency, and overall maritime safety. This study introduces a segmented labeling approach leveraging Large Language Models (LLMs) for ETA prediction, highlighting their advanced reasoning capabilities. Building on recent progress in time-series forecasting, we propose a novel framework that utilizes LLMs for ship trajectory prediction and ETA estimation. The framework employs a few-shot in-context learning approach, structured prompt generation, and iterative refinement mechanisms to enhance prediction accuracy and reliability. Comprehensive experiments on a large-scale maritime dataset demonstrate the framework’s strong performance, particularly for short-distance routes, achieving notable improvements in prediction accuracy compared to traditional and deep learning-based methods. These results suggest that LLMs offer a promising direction for advancing time-series forecasting in the maritime sector.