Proteins perform a wide variety of biological tasks, and their unique amino acid sequences define their roles in living systems. In this study, we apply natural language processing (NLP) methods to protein sequence data to enable faster and accurate classification into functional categories. We examined several machine learning and deep learning techniques, including traditional machine learning methods, linear probabilistic models, and ensemble classifiers. Experimental approaches included machine learning algorithms utilising amino acid concentrations ranging from 1 to 4 g, alongside varying counts for amino acids in a protein sequence for deep learning architectures. Results indicate that ensemble methods, particularly soft voting algorithms, achieved the highest F1-scores of 0.75, but CNNs and LSTMs demonstrated considerable performance, highlighting the necessity for sufficient training data and the management of sequence homology among several classes. This work confirms the use and efficacy of classifying protein sequences with NLP approaches. The knowledge gained here establishes a foundation for future study and enhancements of these technologies, potentially significantly augmenting our ability to analyse and comprehend complex biological datasets.