Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined include deep neural networks (DNNs), support vector machines (SVMs), tree-based ensemble models, genetic algorithms (GAs), and surrogate modeling approaches. Comparative analyses from the literature indicate that ensemble tree methods, such as XGBoost, have achieved predictive accuracies up to $R^2$ = 0.995 in speed–power modeling, marginally surpassing DNN performance, while GA-based structural optimization studies have reported weight reductions exceeding 10%. The findings confirm that no single method is universally superior; rather, effectiveness depends on the problem definition, data quality, and computational resources available. Hybrid strategies that combine physics-based modeling with data-driven learning have demonstrated improved generalization, reduced data requirements, and enhanced interpretability. Practical challenges remain, including limited access to open high-fidelity datasets, the computational demands of complex models, and balancing predictive accuracy with explainability. The review concludes that AI should be employed as a complementary toolkit to augment human expertise, with method selection guided by design objectives, constraints, and integration within the broader ship design process.

Early-stage ship design demands efficient exploration of diverse hull forms under tight geometric and performance constraints. Existing data-driven approaches often rely on a single geometric representation, limiting their ability to capture both global proportions and local shape detail. This paper presents a representation-aware generative framework that learns a shared latent encoding of hull geometry from multiple modalities: surface point clouds, waterline sections, and buttock curves. A conditional multimodal autoencoder embeds these complementary descriptors into a unified latent space, enforcing cross-modal consistency and preserving geometric fidelity. To enable the generation of novel hull forms, a latent diffusion model is trained to sample hull representations conditioned on high-level design parameters, such as principal dimensions and form coefficients. The proposed approach produces watertight and geometrically coherent hull forms that align with user-specified design parameters. Experiments on a synthetic hull dataset demonstrate improved geometric coherence and controllability. The method provides a scalable foundation for performance-aware, data-driven exploration in early-stage ship design.

The ageing global fleet and demanding sustainability goals are driving a growing role for machine learning (ML) and artificial intelligence (AI) in naval architecture. This review examines state-of-the-art AI and ML applications in ship design, focusing on ship hull hydrodynamics and structural design. It highlights the use of advanced models such as deep neural networks (DNNs), support vector machines (SVMs), decision tree ensembles, graph neural networks (GNNs), and physics-informed neural networks (PINNs). DNNs and GNNs demonstrate superior capabilities in capturing complex hydrodynamic and structural behaviours when ample data and computational power are available, whereas simpler models (e.g., SVMs or tree ensembles) often excel with limited data and, in the case of trees, provide greater transparency in predictions. PINNs integrate fundamental physics into neural models, producing physically consistent predictions and alleviating data scarcity, albeit with increased implementation complexity and computational cost. The advantages and limitations of each approach are considered with respect to data availability, computational resources, and design objectives. Ongoing challenges include data scarcity due to limited and confidential datasets, high cost of obtaining high-fidelity data, and the lack of robust validation frameworks for AI models. The review concludes with recommendations for developing generalizable, physics-integrated AI frameworks and underscores the importance of context-aware model selection for future ship design advancements.