A reparameterization-based RUL prediction method that integrated channel decomposition and feature fusion was proposed to address the challenges in remaining useful life (RUL) prediction for aero-engines. Specifically, a channel decomposition and reconstruction module based on wavelet transform was designed to capture dependencies between time and channels while reducing redundancy. By fusing the raw input with the extracted feature information, the model enhanced the preservation of original information and mitigated the loss of critical information during deep network training. Furthermore, a frequency-enhanced channel attention mechanism was introduced to further suppress channel redundancy. A neural network was then employed to estimate the mean and variance of the features, and reparameterization was applied to map them into a Gaussian distribution for sampling-based prediction. In addition, the Whale optimization algorithm (WOA) was used to perform global optimization of key hyperparameters. Experimental validation on the C-MAPSS dataset from NASA demonstrated that the proposed method achieved an RMSE range of [11.58,13.72] and a Score range of [224.78,1025.29], with average decreases of 22.67% and 45.56%, respectively. Moreover, the constructed three-dimensional latent space supported the visualization of engine degradation processes.