<p>Accurate remaining useful life (RUL) prediction is critical for ensuring industrial system safety and effective maintenance; however, it continues to encounter significant challenges, including feature distribution shifts and cross-domain data scarcity. To address these challenges, this study proposes an adversarial enhanced attention domain adaptation network (AEADAN) for robust cross-domain RUL prediction. The framework integrates a Wasserstein generative adversarial network with gradient penalty and a variational autoencoder to perform effective cross-domain data augmentation and extract domain-invariant degradation features. A dedicated multi-component adaptation module integrates domain discrimination with distribution discrepancy minimization to achieve effective feature alignment, while an exponentially decaying weighting mechanism adaptively balances classification and alignment losses throughout training. Extensive experiments conducted on benchmark datasets demonstrate that AEADAN markedly outperforms state-of-the-art methods in terms of prediction accuracy and generalization capability, thereby providing an effective solution for accurate RUL prediction.</p>