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Magnetically coupled resonant wireless power transfer (MCR-WPT) technology offers longer effective transmission distances and higher efficiency compared to traditional charging methods, making it better suited to the prolonged and efficient operation of autonomous underwater vehicles. This paper first establishes a traditional mathematical model and then refines it while analyzing the variations in the self-inductance and mutual inductance of underwater coils. To further enhance the system’s performance, a multi-objective optimization of the coupling mechanism is conducted. An orthogonal experiment is employed to determine the effects of various influencing factors on the coils’ self-inductance and mutual inductance. Subsequently, an RBF neural network is used to create a regression prediction model based on the results of the orthogonal experiment. The NSGA-II algorithm is then applied for the multi-objective optimization of the coupling mechanism, resulting in a Pareto front solution set. The optimized efficiency is 93.35%, representing an approximately 6% improvement over the original system, with the power density increasing from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mrow><msup><mrow><mn>1.267</mn><mo>×</mo><mn>10</mn></mrow><mrow><mn>6</mn></mrow></msup><mo> </mo><mi mathvariant="normal">W</mi></mrow><mo>/</mo><mrow><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></mrow></mrow></semantics></math></inline-formula> before optimization to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mrow><msup><mrow><mn>4.782</mn><mo>×</mo><mn>10</mn></mrow><mrow><mn>6</mn></mrow></msup><mo> </mo><mi mathvariant="normal">W</mi></mrow><mo>/</mo><mrow><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></mrow></mrow></semantics></math></inline-formula> after optimization. Significant enhancement in system performance is achieved.