A significant push toward sustainability has emphasized the need to make all vessels as energy efficient as possible. Close attention must be paid to the performance evaluation of the vessel’s main energy consumer, particularly the engine. Moreover, prioritizing the efficient operation of the engine is essential for maintaining the vessel’s operational safety. Engine performance evaluation has been conducted using various methodologies, based on either field testing data or physical and data-driven models. This research focuses on developing a condition monitoring model that is effective even with limited data availability, highlighting the correlation between subsystem degradation and its impact on the overall underperformance of the engine. The literature review examines potential methods for condition monitoring of marine diesel engines, with a focus on their modeling purposes. These methods are classified based on their theoretical foundation: White-box models, which are based on first principles, and Black-box models, which rely on the interrelationships between input and output parameters using available mathematical tools. Additionally, the literature discusses Grey-box models, which combine physics-based principles with data-driven approaches. The modeling purpose of the methods analyzed is identified as either parameter estimation or fault detection and isolation, depending on the available data and the chosen methodologies. To achieve the objective of this thesis, a structured model development process is formulated. This process begins with defining the available parameters and determining which are inputs and which are outputs. The dataset then undergoes pre-processing steps before being divided into training, validation, and testing datasets. The modeling procedure begins by developing a basic model using shop test data (manufacturer data) to benchmark target parameters and determine whether a data-driven or hybrid model is necessary for engine condition monitoring. A neural network is then employed to capture data variance and leverage its ability to identify non-linear relationships. Building on the baseline neural network model, the loss function is customized to incorporate physics-based information into the data-driven model. This integration is achieved using two methods: either by approximating shop test data to represent engine behavior or by incorporating physical equations derived from mean value engine models. These physicsbased elements are introduced as new loss terms within the loss function evaluation. Each of the five developed model variations is then validated for their predictive capacity, with evaluation metrics compared to identify the most promising approach. Additionally, the models are assessed for their ability to generalize to unseen data. After concluding the modeling process, a case study was developed to examine the practical application of the best model in creating a condition monitoring tool. This tool relies on monitoring the residual trends of target parameters and correlating these trends with potential fault types in engine subsystems. By analyzing these residual trends, the model can effectively link subsystem degradation to overall engine underperformance, providing valuable insights for fault detection. Transitioning to the conclusions, it becomes clear that incorporating physics-based information, particularly from mean value models, yields superior results in capturing the variance of actual measurements and accurately predicting different operational conditions. This highlights the critical importance of developing hybrid approaches that combine the strengths of data-driven models while addressing their inconsistencies, thereby producing results closer to the ground truth. Further recommendations include integrating recurrent neural networks and establishing a decision support system to help identify the root causes of degradation and suggest necessary actions. Overall, developing a condition monitoring model proves beneficial for identifying underperformance and ensuring the correct operation of the vessel.