The maritime industry, essential for international logistics and trade, increasingly requires precise and reliable control systems for surface vessels, especially during critical operations such as docking and maneuvering in tight areas. This research presents a data-driven framework for predicting ship control commands, specifically focusing on the number of turns and rudder angle, by using hybrid models that combine deep learning and ensemble machine learning techniques. CNNs are used for feature extraction, while recurrent architectures like GRU and LSTM are used to handle the time-series data generated by a three-DOF vessel model operating in a simulated port environment. To improve prediction performance, these deep learning models are combined with Random Forest and Extra Trees regressors. Comparative evaluations show that the GRU+ExtraTree model delivers superior accuracy and responsiveness, particularly during sudden changes in control signals, due to its ability to capture temporal dependencies. The proposed method shows promising potential for real-time trajectory tracking and autonomous docking applications in maritime navigation.