In this paper, we propose a new hybrid model, called AVE, that integrates the strengths of Autoencoder (AE) and Variational Autoencoder (VAE) to enhance outlier detection for numerous high-dimensional datasets. The proposed architecture leverages the reconstruction strengths of AE and the regularized latent space of VAE to build a stable framework for anomaly detection. Our experimental evaluation is conducted on 16 standard test sets from various domains. The results show that the AVE architecture outperforms the standalone architecture of AE, VAE, and other compared algorithms. The AVE architecture, specifically, achieves an average precision of 0.6925 and an average ROC-AUC of 0.8902 across the subsets of this test set. It surpasses the second-best architecture by 25.99% for precision and 5.47% for ROC-AUC. It surpasses AE and VAE architectures by 67.26% and 45.44% in precision, and by 7.71% and 10.03% in ROC-AUC, respectively. It achieves the best accuracy on 12 of the 16 datasets and the optimal ROC-AUC on 5 of them. Our findings demonstrate that AVE is a more reliable and precise method for detecting outliers, particularly in complex, high-dimensional datasets, making it an effective solution for real-world applications.