Topic analysis is also known as topic detection or topic extraction, refers to ML method that categorizes larger text datasets into categories based on the individual text. It employs natural language processing to analyze human communication by breaking it down into components such as speech, words, sentences, and context, aiming to identify patterns and unveil underlying meanings within texts. This process aids in deriving insights and facilitating data-driven decisions. Within topic analysis, the primary machine learning techniques employed areas of focus include topic modeling and topic classification within this field. However, topic modeling encounters various challenges, specific to document properties. NLP is an integrative subject that merges CS, AI, and linguistics to construct systems capable of comprehending and processing human language. The prevalent machine using labeled data to categorize unlabeled data. This process relies on the knowledge gained during training to classify new data. In general, text classification methods handle predefined and finite categories such as predicting labels like credible or not credible for credibility assessment, or determining movie ratings (bad, okay, good) based on reviews. The difficulty in text classification arises from the predetermined set of topics or labels. When the topics are not known in advance, the concept of topic modeling becomes crucial. This statistical modeling approach is designed to identify abstract topics within a set of documents that lack predefined labels. By analyzing labelled data, this method extracts underlying topics.