This paper introduces an innovative hybrid method for the classification of Gleason Scores (GS) in prostate cancer using Whole Slide Images. The researchers combine Deep Learning and Machine Learning (ML) techniques to automate the precise classification of GS. They employ a specially designed Variational Autoencoder for feature extraction, utilizing a pre-trained VGG16 Convolutional Neural Network to build the encoder. Principal Component Analysis is then used to reduce the dimensionality of the feature vector to 50 significant features for further Gleason Grade classification. The study uses the SICAPv2 database and evaluates feature importance with Shapley Additive explanations (SHAP). Comparative analysis of five ML techniques and two custom-designed Deep Neural Network (DNN) architectures shows that the Support Vector Machine algorithm with hyperparameter tuning and the custom-designed five-layer DNN architecture achieved accuracies of 84% and 89%, respectively, outperforming other ML models.