This review discusses motion prediction for intelligent ship navigation, focusing on the challenges and methods involved in predicting ship motion across different time scales. As the maritime environment becomes more complex, accurate motion prediction is essential for enhancing navigation safety, reducing collision risks, and optimizing routes. Ship motion prediction is a critical component of the Guidance, Navigation, and Control (GNC) system, with different time scales used for various applications, such as long-term traffic flow management, short-term collision avoidance, and extreme short-term motion control. Ship motion prediction models can be based on either mechanics or data, and they are used for collision-avoidance path planning, path tracking control, traffic flow simulation, and long-distance route optimization. Recent research has increasingly incorporated Automatic Identification System (AIS) data, artificial intelligence, machine learning, and deep learning techniques to improve the accuracy of ship motion prediction. These methods help in enhancing navigation safety and collision avoidance applications. The ship navigation environment includes hydrological, meteorological, topographical, and traffic conditions. Environmental factors affecting ship navigation include both static and dynamic aspects. Static factors include navigational water depth, shorelines, islands, reefs, and obstacles, while dynamic factors include wind, waves, currents, visibility, traffic flows, and ship behavior characteristics. These factors are modeled and predicted using ocean and weather forecasts, traffic flow predictions, ship behavior detection, and real-time data from onboard sensors. Ship navigation behavior and traffic flow modeling involve analyzing historical data and using simulation methods to replicate traffic flow. AIS data are commonly used for extracting waterway traffic elements, clustering ship behaviors, and predicting ship behaviors. These data are crucial for maritime traffic services and collision risk assessments. Ship motion models are classified into hydrodynamic models and responding models. Hydrodynamic models include multiple linear and nonlinear hydrodynamic parameters and disturbance coefficients, while responding models represent another form of mathematical ship-motion model. These models are used for ship maneuvering and navigation control, with different mathematical models suitable for different scenarios. Ship motion prediction methods include black-box models based on neural networks, data, and similar techniques, which can take into account a wider range of inputs and provide higher predictive accuracy. However, these models are typically trained using complex mathematical algorithms and are difficult to understand and explain. Ship extreme short-term motion prediction focuses on the immediate impact of the current environment on the ship's motion to predict its state. Methods using mathematical models for the prediction of ship motion mainly consist of linear and nonlinear predictive methods. Prediction methods based on mathematical models of ship motion can predict certain aspects of a ship's motion state, but due to the nonlinear and time-varying nature of ship-motion models, complex motion states require more sophisticated and accurate motion models. Short-term ship-motion prediction involves collision-avoidance planning algorithms, especially for short-time and short-distance navigation re-planning. These algorithms are crucial for avoiding collisions among autonomous ships and ensuring safe navigation. Long-term trajectory prediction based on estimated ship traffic flow, historical trajectories,This review discusses motion prediction for intelligent ship navigation, focusing on the challenges and methods involved in predicting ship motion across different time scales. As the maritime environment becomes more complex, accurate motion prediction is essential for enhancing navigation safety, reducing collision risks, and optimizing routes. Ship motion prediction is a critical component of the Guidance, Navigation, and Control (GNC) system, with different time scales used for various applications, such as long-term traffic flow management, short-term collision avoidance, and extreme short-term motion control. Ship motion prediction models can be based on either mechanics or data, and they are used for collision-avoidance path planning, path tracking control, traffic flow simulation, and long-distance route optimization. Recent research has increasingly incorporated Automatic Identification System (AIS) data, artificial intelligence, machine learning, and deep learning techniques to improve the accuracy of ship motion prediction. These methods help in enhancing navigation safety and collision avoidance applications. The ship navigation environment includes hydrological, meteorological, topographical, and traffic conditions. Environmental factors affecting ship navigation include both static and dynamic aspects. Static factors include navigational water depth, shorelines, islands, reefs, and obstacles, while dynamic factors include wind, waves, currents, visibility, traffic flows, and ship behavior characteristics. These factors are modeled and predicted using ocean and weather forecasts, traffic flow predictions, ship behavior detection, and real-time data from onboard sensors. Ship navigation behavior and traffic flow modeling involve analyzing historical data and using simulation methods to replicate traffic flow. AIS data are commonly used for extracting waterway traffic elements, clustering ship behaviors, and predicting ship behaviors. These data are crucial for maritime traffic services and collision risk assessments. Ship motion models are classified into hydrodynamic models and responding models. Hydrodynamic models include multiple linear and nonlinear hydrodynamic parameters and disturbance coefficients, while responding models represent another form of mathematical ship-motion model. These models are used for ship maneuvering and navigation control, with different mathematical models suitable for different scenarios. Ship motion prediction methods include black-box models based on neural networks, data, and similar techniques, which can take into account a wider range of inputs and provide higher predictive accuracy. However, these models are typically trained using complex mathematical algorithms and are difficult to understand and explain. Ship extreme short-term motion prediction focuses on the immediate impact of the current environment on the ship's motion to predict its state. Methods using mathematical models for the prediction of ship motion mainly consist of linear and nonlinear predictive methods. Prediction methods based on mathematical models of ship motion can predict certain aspects of a ship's motion state, but due to the nonlinear and time-varying nature of ship-motion models, complex motion states require more sophisticated and accurate motion models. Short-term ship-motion prediction involves collision-avoidance planning algorithms, especially for short-time and short-distance navigation re-planning. These algorithms are crucial for avoiding collisions among autonomous ships and ensuring safe navigation. Long-term trajectory prediction based on estimated ship traffic flow, historical trajectories,