Bottlenecks can occur at ports when vessel and train schedules are desynchronized, creating operational inefficiencies and excessive dwell times. This project focuses on the optimization of rail operations at the Port of Montreal, providing shared and advanced visibility to all ecosystem partners and enabling data-driven decision making, predictions and planning scenario analyses. The solution combines machine learning techniques for prediction tasks with operations research for the optimization of port planning and operations