Accurate prediction of waves is crucial for the provision of marine-related activities, such as harbor operations, naval navigation, and numerous coastal and offshore ventures. Dependable forecasts of the wave state are essential to ensure safe operations, streamline maritime logistical procedures, and minimize dangers related to a challenging-to-predict sea state. In this project, four models are implemented: Decision Forest Regression (DFR), Neural Network Regression (NNR), Boosted Decision Trees Regression (BDTR), and Linear Regression (LR). The primary objectives of the project are to (i) implement four different prediction models independently on an ocean wave prediction dataset, and (ii) determine the best prediction model among the four in predicting ocean waves. Six evaluation metrics are used to measure the models’ performance, and the results indicate that BDTR performs better across all evaluation metrics, achieving the highest coefficient of determination (COD) and accuracy values, as well as the lowest error measurements. NNR was the worst, with the lowest COD and the highest errors. DFR and LR models were provided with moderate accuracy.