Founded in 1910, Boskalis, a leader in offshore operations, contends with limitations in ABB's Ability Marine Advisory System 'OCTOPUS' in predicting maximum vessel motions for heavy-transport vessels (HTVs). Accurate prediction of these motions, especially roll and pitch, is vital for transporting large, heavy structures, as exceeding predefined limits can jeopardize both vessel and cargo integrity. OCTOPUS's challenges, stemming not only from its reliance on linear theory but also potentially from the quality of its environmental data, underline the need for exploring alternatives, such as Machine Learning (ML) approaches, adept at handling complex, nonlinear phenomena, to ensure operational safety and efficiency. This thesis presents the development and comparison of three new approaches to predict maximum roll and pitch motions. The approaches are compared and evaluated against OCTOPUS. Two validation strategies are used to test their performance under known and unknown loading conditions (LCs). Known LCs in this context refer to the evaluation of data that incorporate LCs that are included in the training dataset for ML-based approaches. On the other hand, unknown LCs refer to the evaluation of data that incorporate LCs that are not included in the training dataset for ML-based approaches. The approaches are trained and validated using sensor data, LC data, and environmental data from 24 different voyages for a specific HTV. They differ in their design and the type of environmental data they use. The superior performance of ML-based approaches over OCTOPUS in known LCs is mainly due to two factors. First, ML approaches inherently incorporate nonlinear phenomena, which is particularly effective in accurately predicting maximum roll motion. Second, they are better equipped to handle flaws in environmental data. Although these advantages contribute to a significantly lower mean absolute percentage error (MAPE) compared to OCTOPUS, ML-based approaches face challenges in unknown LCs and extreme motion response scenarios. However, it is noteworthy that ML approaches quickly adapt to unknown LCs when small portions of these LCs are included in the training dataset. ML shows potential in vessel motion prediction, and this thesis underscores the importance of diverse training data to enhance its reliability in unknown LCs and extreme motion response scenarios. For Boskalis, addressing these challenges with strategies such as adjusting the custom loss function, data augmentation, and implementing ensemble methods could improve the accuracy of these approaches. This progress is significant for Boskalis and the wider maritime industry, paving the way for adaptive and efficient prediction systems. Collaborative efforts between industry and academia, using rich data and expertise, are essential to drive these innovations.