Methylated DNA is an essential epigenetic transition. It has an impact on the stabilization of genes. From DNA sequences, finding out the precise classification of methylation states is vital yet complex because of the intricate structure of these patterns. This chapter compares various deep learning models. The comparison has been done based on the performance of the models. Deep architectures applied are Bidirectional Long Short-Term Memory (BiLSTM), Extreme Gradient Boosting (XGB) Classifier, Gradient Boosting Classifier, Recurrent Neural Network (RNN), Logistic Regression, Random Forest, and K-Nearest Neighbor Classifier (KNN) for binary classification of methylated and unmethylated gene segments. The study denotes that BiLSTM outperforms all the models in the context of accuracy and sequence influence. Because of this, BiLSTM is considered the most effective model with an accuracy of 94.62% and also promotes the application of deep learning in epigenetics. These observations have essential effects on research in genetics. This chapter provides a comparative evaluation, offering valuable perspectives for bioinformatics.