Natural language processing (NLP) which is a branch of Artificial Intelligence (AI) has experienced significant improvement in the recent past to allow machines to language comprehend and generate. They consist of uses like machine translation, sentiment analysis, chatbots, and virtual assistants, which form a cornerstone part of life. However, even with these advances, NLP still has numerous critical difficulties that affect its proficiency and usefulness in applying the systems. Some of the major problems include one language may mean different things to different people; every situation requires different approaches; and finally people from different cultures and languages will pose a significant problem. This paper presents evidence of lexical, syntactic, and semantic ambiguities that complicate the language understanding process. Besides that, NLP models are not able to comprehend the flow of human’s dialogues which is important factor of the communication. The problem of language diversity in human dialogue makes it even more challenging to develop NLP since over 7,000 languages are characterized by unique structures and expressions. With the recent development in Machine learning, and deep learning, these challenges have been well addressed. Pretrained transformer models like BERT and GPT have greatly enriched the field’s tech arsenal, since language comprehension and Boolean modernity loops very difficult to model and tackle. This journal provides a comprehensive look at these issues presents current technologies and examines new trends pertaining to NLP. Lastly, it brings focus on way ethical concerns like bias are paramount in making current NLP systems neutral. Looking forward more advances are expected in NLP which has the prospective of further improvement of interaction between human and computer.