Missing data is a common challenge in large-scale epidemiological datasets, such as the National Health and Nutrition Examination Survey (NHANES), which is extensively used for public health research and policymaking. In this study, we systematically compare classical imputation methods—namely mean imputation, K-nearest neighbors (KNN), and multivariate imputation by chained equations (MICE)—with deep generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Wasserstein GANs (WGANs), for their ability to accurately reconstruct missing values. Our framework employs a common scaling approach using StandardScaler and simulates missingness to evaluate reconstruction performance via Root Mean Squared Error (RMSE) and the Kolmogorov–Smirnov (KS) statistic. Special emphasis is placed on variables with high entropy, which are hypothesized to be more challenging and informative for imputation tasks. Results indicate that while MICE outperforms simpler imputation techniques in terms of RMSE, the VAE achieves superior distributional matching as evidenced by lower RMSE values compared traditional techniques, GAN and WGAN. These findings highlight the potential of deep generative models for improving the quality of imputation in complex biomedical data.