Secure covert communication and medical applications benefit significantly from using Reversible Data Hiding (RDH) techniques, which allow perfect recovery of both the original media and the embedded data. Over the past few years, several pixel prediction methods have become popular for enhancing Reversible Data Hiding (RDH) methods. We systematically compare different predictive methods, ranging from traditional statistical classical methods to state-of-the-art machine learning-based methods that include Convolutional Neural Network (CNN) and Generative Adversarial Networks (GAN). The evaluation of these predictors is conducted in terms of quality metrics, which include Mean Square Error (MSE), Variance, and Mean prediction accuracy. Our proposed work illustrates that these types of predictors based on machine learning yield better performance than classic-based approaches by combining deep feature extraction and content-based learning, which enhances the precision and security of the extract data embedding.