Efficient and sustainable transportation systems are crucial. This paper introduces an innovative approach to optimizing ship scheduling in waterborne public transportation by leveraging deep learning-driven demand prediction. Employing the Temporal Fusion Transformer (TFT) model for superior forecasting performance, the study integrates dynamic and static data inputs to predict transportation demand accurately. A two-stage methodology comprising a TFT-based demand prediction model and a scheduling decision module is proposed, utilizing a mathematical model aimed at cost minimization. The model’s effectiveness is demonstrated through real-world data, showcasing optimized capacity adjustments that align closely with actual demand, thus reducing scheduling discrepancies and enhancing service quality. This work signifies a pivotal step toward utilizing deep learning for maritime transportation management, emphasizing the potential economic and societal benefits of AI applications in this field.