A key area of machine learning called sentiment analysis seeks to extract subjective data from textual evaluations. The most popular technique for anticipating user ratings is sentiment analysis, and several machine-learning techniques have been employed to provide precise predictions. Sentiment analysis is the skill of examining information regarding what the general public really thinks about your company, a text, an opinion, a social media post, etc. It is a very potent tool in the analytics toolbox. Natural language processing and text mining both have a close connection to the study of sentiment. It can be used to evaluate the reviewer's viewpoint on certain topics or the review's overall polarity. The accuracy of the model is evaluated using sentiment analysis on the IMDB movie reviews dataset utilizing machine learning (ML) and natural language processing (NLP) techniques. Natural language processing and machine learning combine to provide the fundamental building blocks of sentiment analysis. Provides context to grasp the meaning of any text by enhancing the capabilities of machine learning and natural language processing. Using machine learning classification methods, this study suggests a prediction model for the sentiment analysis of movie reviews. This study aids researchers in choosing the most effective method for doing accurate and timely emotive analysis on IMDB movie reviews. Here, want to estimate the general polarity of the review using machine learning and natural language processing (NLP).