The field of natural language processing (NLP) is that of artificial intelligence and computer science, which investigates how computers influence human language. Speech recognition, translation, and sentiment analysis have all been improved because to the application of machine learning in natural language processing. It has become more difficult for computers to comprehend, interpret, and synthesize human language. Recent developments in machine learning for natural language processing (NLP) have centered on the creation and implementation of intricate algorithms and models that assist machines in deciphering and producing human language in a more speedy and effective manner. First, we will go over the fundamentals of natural language processing and machine learning. NLP will continue to develop, passing through rule-based systems, statistical models, and most recently, deep learning. Having a strong understanding of context, semantics, and syntax, these models make it possible to develop increasingly complex applications for language analysis. In this paper, the confines and restrictions that are associated with NLP machine learning approaches are discussed. An example of a problem is the necessity for computational resources, as well as interpretability and data bias. When we talk about continuing efforts to address these problems, we talk about things like research into transfer learning and few-shot learning, as well as the development of models that are more efficient and resilient. Our final discussion examines natural language processing research's future. Multimodal data (text, audio, and visual) and interactive and customizable NLP systems are conceivable. To conclude, the study emphasizes machine learning's revolutionary impact on human language research and applications. Further innovation and growth in this area are possible. © 2025 Elsevier B.V., All rights reserved.