Quick and precise assessment of the remaining service life of lithium-ion batteries is considered to be of substantial importance for enhancing the reliability of equipment. Traditional Gaussian process regression (GPR) is found to exhibit a poor hyperparameter optimization effect and is deemed difficult to optimize. The sparrow search algorithm (SSA) was employed to enhance the optimization of hyperparameters within Gaussian process regression. Additionally, wavelet packet decomposition (WPD) was utilized to reduce the complexity of the dataset, extract relevant information, and improve prediction accuracy. A model for predicting the remaining useful life (RUL) was proposed, which combined wavelet packet decomposition, Gaussian process regression, and the sparrow search algorithm (SSA). Firstly, the equal voltage drop discharge time curve was used as the indirect health factor, while the battery capacity was used as the direct health factor. The correlation between the two was verified using the Pearson coefficient. Second, the wavelet packet decomposition was utilized to decompose both the direct and indirect health factors. High-frequency and low-frequency signals were extracted and divided into the training set and the test set. Then, a Gaussian process regression model was established. SSA was used to optimize the hyperparameters of this model. Different signals were predicted and superimposed respectively to achieve accurate prediction of the remaining service life. Finally, validation was carried out using the public dataset. The results demonstrate that the average absolute error of the model proposed in this paper is not exceeded by 0.006 5, the average absolute percentage error is not exceeded by 0.005 2, and the root-mean-square error is not exceeded by 0.007 8, being characterized by good prediction accuracy and generalizability.