## 5 Conclusions  
The determination of a ship's main particulars during the design phases constitutes a significant and intricate process, necessitating the evaluation of relationships among numerous parameters. Traditionally, these relationships are established through statistical methods or empirical formulas based on data from existing ships. However, conventional statistical methods and empirical formulas often depict relationships between two factors in isolation, disregarding others. Consequently, novel approaches have gained prominence in the determination of a ship's main particulars. In this study, a model employing a Bayesian network and non-linear regression was developed to predict the primary characteristics of a Z-Drive harbor tugboat equipped with azimuthing propulsors capable of meeting the specified bollard pull and speed requirements. The case study utilized a dataset comprising main particulars from 68 distinct azimuthing-propulsor-equipped Z-Drive harbor tugboat series, derived from a collection of over 200 existing tugboats, with sister ships eliminated. The dataset was analyzed using the developed model, and the results were compared. Upon examining the distribution of the bollard pull input relative to the other output parameters in the calculated results, it was observed that the accuracy of the results was higher within the 60 to 80 metric tons bollard pull range, while deviations were noted for the maximum and minimum bollard pull values. In conclusion, the developed model proves most effective for the early design of tugs with bollard pull capacities ranging from 60 to 80 metric tons. The results of the case study, the presented descriptive methodology, and the developed model collectively emerge as crucial tools for decision makers and ship designers in the conceptual design process for determining a ship's main dimensions. Furthermore, this study demonstrates that machine learning methods can be applied to the design of ship types with unique parameters, such as tugboats designed for specific purposes rather than cargo-carrying ships. Neural network models, exemplified by the Bayesian network in this study, present themselves as viable alternatives to traditional statistical and empirical methods. This model proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision returns possibilities in further stages of ship design like expensive Computational Fluid Dynamics (CFD) and conventional ship model tests. Therefore, the neural networks streamline this process by offering faster and more cost-effective predictions compared to traditional methods, which entail time-consuming and expensive extensive physical testing and computational simulations, thus diminishing the necessity for expensive prototyping and testing. This approach not only reduces costs at the preliminary design stage but also enhances time efficiency and mitigates risks which are associated with proposed designs. The utilization of machine learning methodologies, particularly neural network models such as the Bayesian network, in the design of ships with unique parameters like tugboats is poised to yield numerous advantages, including enhanced accuracy, expedited design iterations, adaptability, and improved time and cost efficiency. These advancements herald an innovative shift in ship design, enabling the utilization of data-driven insightsto develop vessels that are safer, more efficient, and technologically advanced, tailored to meet contemporary design requirements. The models proposed within this research are dynamic in nature and specifically tailored for tugboats. However, in forthcoming studies, these models have the potential to be modified for various other vessel types. Achieving this adaptation necessitates updating the inputs in accordance with the specific characteristics of the target ship type.