Global ship path planning in complex maritime environments is challenged by dynamic disturbances, vessel-specific constraints, and long-range trajectory dependencies. This study develops an integrated hybrid planning framework that combines deep generative modeling with rule-based optimization. Automatic identification system trajectory time series are first transformed into Gramian Angular Field images to enhance spatio-temporal feature extraction. Vessel type and length are encoded as one-hot vectors and introduced as conditional variables, enabling personalized path generation. These inputs are processed by a Multi-Head Attention–based Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (MHA-cWGAN-GP), in which multi-head attention is used to model long-range dependencies, and conditional Generative Adversarial Network (cGAN) training together with a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) objective is adopted to improve conditioning behavior and training robustness. The model generates initial navigation paths, which are further refined using an A* search procedure that incorporates wind and current disturbances, as well as constraints such as static obstacles, water depth, and Traffic Separation Scheme (TSS) regulations. The final path is smoothed to ensure feasibility and compliance. In case studies for the Ningbo–Zhoushan Port and Yangtze River Estuary, the hybrid planner reduces the number of search nodes from 45 to 57 to 29–35 while simultaneously enforcing TSS, water-depth, wind, and current constraints, with only about a 3–4% increase in path length relative to classical A* and Dijkstra algorithms. The results indicate that the proposed framework effectively integrates learning and optimization, offering a practical and intelligent solution for real-world maritime path planning.
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