Abstract: Objectives In response to the problems of high computational cost and difficulty in global optimization in traditional methods under high-dimensional (30-dimensional) full-ship complex optimization scenarios, this study explores a systematic optimization method based on surrogate models to achieve accurate prediction and efficient optimization of ship resistance performance. Methods The Free-Form Deformation (FFD) technique is employed to achieve 30-dimensional high-degree-of-freedom hull parameterization. A sample set is generated using Latin Hypercube Sampling and resistance data are obtained through CFD simulations; subsequently, a high-accuracy Kriging surrogate model is constructed, integrated with a genetic algorithm for global optimization in the surrogate model space, and sensitivity analysis is conducted to identify key design variables. Results Optimization results based on the KCS container ship model indicate that the prediction error of the surrogate model is only 0.35%, and the resistance coefficient of the optimized hull is reduced by 8.52% compared to the original hull. Sensitivity analysis shows that the bulbous bow region has significant sensitivity to resistance performance. Conclusions This study achieves a full-process closed-loop validation of FFD parameterization, Kriging surrogate modeling, and genetic algorithms under high-dimensional full-ship optimization scenarios, providing a reproducible example for the application of mature technical routes in complex engineering problems. The obtained drag reduction and identification of key variables offer clear engineering guidance value.