Many traditional and state-of-the-art ship routing methods rely on single-objective formulations, deterministic weather inputs, or fixed operational assumptions, which may lead to suboptimal or impractical routing decisions under realistic and uncertain marine environments. This study presents an ensemble-based multi-objective optimization framework for ship route planning under uncertain weather conditions. The framework integrates a neural network model, trained on real onboard ship performance data and tuned using Bayesian hyperparameter optimization, to predict fuel consumption based on ship speed and marine weather parameters. An ensemble of weather forecasts is assigned to route waypoints using a bootstrapping method, enabling the evaluation of multiple cost functions reflecting trade-offs between voyage time, fuel consumption, and safety. Four optimization objective strategies — ensemble mean, worst-case, risk-aware, and Conditional Value-at-Risk (CVaR) — are implemented within a Grey Wolf Optimizer (GWO) to derive optimal routes across various voyages. The results demonstrate notable variations in route performance based on the selected strategy. For example, the CVaR approach achieves a balance between robustness and efficiency, with voyage fuel consumption for the longest journey (Voyage 3) reaching 490,475 kg, while the worst-case strategy prioritizes risk-averse paths, resulting in the highest fuel usage at 505,308 kg. Conversely, the ensemble mean strategy offers the lowest average fuel consumption (474,078 kg) but may expose the voyage to higher uncertainty. Furthermore, the proposed GWO demonstrates high precision in schedule adherence, maintaining arrival time deviations within a 30-minute margin across all optimized voyages, thereby justifying its effectiveness in handling complex multi-objective constraints. The developed framework is applicable to real-time voyage optimization and can support ship operators in achieving fuel efficiency and safety under varying ocean conditions.