Detecting outliers within MetOcean datasets is crucial for identifying extreme weather conditions to facilitate informed decision-making whilst various outlier detection approaches and methods are available to be selected. However, choosing the most appropriate one requires an in-depth understanding of their capabilities and this poses a great challenge to the novice within the domain. Therefore, this study is proposed to empirically compare these approaches and provide insights on their performance when dealing with large MetOcean data. The comparison is driven by three outlier detection approaches using the SEAFINE dataset: Extreme Value Analysis (EVA), Functional Data Analysis (FDA), and Unsupervised Learning (UL). A framework is designed to systematically conduct the comparative analysis, which involves five phases: data preprocessing, model training, outlier detection, parameter estimation, and evaluation. This analysis provides insights into the performance of each outlier detection method for each approach and the comparison between them. Overall, the results indicate that the EVA approach exhibits the fastest average execution time compared to the FDA and UL. Within the EVA approach, Peak-Over-Threshold is the most effective, closely aligning the distribution of extreme values with the estimated distribution. Functional Boxplot emerges as the optimal method for FDA, capable of detecting outliers far from the median. Cluster-Based Local Outlier Factor (CBLOF) is the most effective UL method, predicting outliers close to the expected total, with high Excess-Mass values and fast execution.