The integration of machine learning and deep learning techniques has significantly advanced natural language processing (NLP) capabilities in healthcare learning systems. This paper explores recent developments in the application of these technologies to enhance the efficiency and effectiveness of healthcare-related language tasks. The utilization of machine learning algorithms, such as support vector machines, random forests, and neural networks, has proven invaluable in extracting meaningful insights from unstructured healthcare data. Deep learning models, particularly recurrent neural networks (RNNs) and transformers, have demonstrated exceptional performance in handling sequential and contextual information inherent in medical texts.