To address the insufficient prediction accuracy of traditional insulation lifetime prediction methods caused by the neglect of coupling correlation information among multiple degradation parameters, this paper proposes a remaining life prediction method for transformer oil-paper insulation based on the three-dimensional C-vine Copula function, aiming to fully explore the capability of coupling correlation information among degradation parameters in characterizing insulation degradation behavior. First, a single-parameter degradation model of oil-paper insulation is constructed using a nonlinear Wiener process to capture the uncertainty and nonlinear characteristics inherent in the degradation behavior of oil-paper insulation. Second, a three-parameter joint probability distribution model for oil-paper insulation is developed based on the three-dimensional C-vine Copula function structure. This model extracts the latent degradation features embedded in the coupling correlation information of multiple parameters and enables precise characterization of the degradation trajectory. Finally, the Markov chain Monte Carlo (MCMC)-Gibbs sampling algorithm is employed to estimate the unknown parameters of the model, thereby achieving the remaining life prediction of transformer oil-paper insulation. Furfural, methanol, and 5-hydroxymethylfurfural are selected as insulation degradation parameters, and the reliability under both independent-parameter conditions and three-parameter coupled conditions is evaluated. The corresponding remaining life predictions are compared and analyzed. The results indicate that the proposed method can more accurately predict the remaining service life of oil-impregnated paper insulation.