NLP is a branch of artificial intelligence that en-compasses various tasks related to language, enabling computers to communicate with people in a human-like manner. NLP facilitates functions such as text reading, voice comprehension, analysis, and key point identification. Analyzing human language requires employing diverse methodologies within NLP, ranging from statistical and machine learning techniques to rules-based and algorithmic approaches. This research paper highlights a few approaches like LSTM, Seq2Seq models, Neural Machine Translation, Acoustic Modelling, and Connectionist Temporal Classification. The need for a wide range of methodologies arises due to the varying nature of text- and voice-based data and the diverse practical applications of NLP. Although NLP presents challenges, significant progress has been made in recent years. With the continuous development of computers, they are approaching the ability to comprehend basic human language.