The primary objective of this project is to forecast significant wave height along the coasts of Ireland. The project encompasses a comprehensive data preprocessing phase, involvibg data cleaning, feature engineering, datetime index standardization, and stationarity adjustments to ensure the dataset's quality and consistency. The Dataset includes historical wave measurements such as height, direction, speed, sea temperature, and geographical information rocorded for 3 years. We aimed to develop a robust predictive model that could provide reliable estimates of wave heights for specific locations over a 3-month period. Leveraging advanced machine learning algorithms, and employing time series techniques, we constructed a model capable of learning intricate data patterns and relationships. Among the tools and techniques utilized were the XGBoost algorithm, STL decomposition, datetime standardization, and resampling. Our developed model exhibited a remarkable capacity to detect hourly time series fluctuations and deliver accurate predictions for a 3-month horizon. Finally, testing and validation processes were carried out to ensure the model's reliability and generalization to unseen data. The successful completion of this project holds immense potential for maritime safety, coastal management, and various ocean-related activities, offering stakeholders valuable insights to make informed decisions based on accurate wave height predictions.