This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP) to overcome the current shipping industrial challenges. The SEEMP enforces various emission control measures, where ship energy efficiency should be evaluated by collecting vessel performance and navigation data. That information is used to develop the proposed data analytics that are implemented on the engine-propeller combinator diagram (i.e. one propeller shaft with its own direct drive main engine). Three marine engine operating regions from the initial data analysis are noted under the combinator diagram and the proposed data analytics (i.e. data clustering methodology) to capture the shape of these regions are implemented. That consists of implementing the Gaussian Mixture Models (GMMs) to classify the most frequent operating regions of the marine engine. Furthermore, the Expectation Maximization (EM) algorithm is used to calculate the respective parameters of the GMMs. This approach can also be seen as a data clustering algorithm that facilitated by an iterative process for capturing each operating region of the marine engine (i.e. the combinatory diagram) with the respective mean and covariance values. Hence, these data analytics can be used in the SEEMP to monitor the performance of a vessel with respect to the marine operating regions. Furthermore, it is expected to develop advanced mathematical models of ship performance monitoring under these operational regions of the marine engine as the future work.