Proteins and carbohydrates are fundamental biomolecules that play crucial roles in life processes. The interactions between these molecules are essential for various biological functions, including immune response, cell activation, and energy storage. Therefore, understanding and identifying protein-carbohydrate binding regions is of significant importance. In this study, we propose ProtCB-Bind, a computational model for predicting protein-carbohydrate interactions. ProtCB-Bind leverages an ensemble of machine learning classifiers and utilizes a common averaging approach to form predictions. The proposed model is trained using a combination of sequence-based and evolutionary-based features of protein sequences, as well as the physicochemical properties of amino acids. To enhance predictive performance, ProtCB-Bind incorporates features derived from recent advancements in transformer-based Natural Language Processing (NLP) for proteins. ProtCB-Bind was designed by systematically identifying the best combination of classifiers and features, and was evaluated using a state-of-the-art benchmark dataset. Its performance was compared against established predictors, including SPRINT-CBH, StackCB-Pred, and StackCB-Embed. ProtCB-Bind outperformed these state-of-the-art predictors, achieving an approximate 3 % improvement in overall performance on benchmark dataset. The sources code for ProtCB-Bind is available at https://github.com/Divnesh/ProtCB-Bind.