Cyberspace has elevated business insights and created a virtual space to store all forms of information online. Due to the rapid development in the online world, the usage of digital documents has increased because it is comfortable for the users to share, update or keep track of the records in one place without losing data. However, maintaining massive data does not suit optimal decision-making and is extremely expensive for storage, processing, and collection. There is a gigantic possibility that human annotators make errors while classifying data because of distraction, monotony, fatigue, and failure to meet the requirements. Once the text classification method uses machine learning approaches, the process will execute with fewer mistakes and more accuracy. The main goal of this review paper is to highlight and explain the role of different machine learning methodologies in text classification. Concurrently, this paper describes the challenges faced by other machine learning techniques and text representation. Furthermore, this review paper will provide an extensive survey on how various machine learning techniques such as Neural Networks, Naive Bayes, Logistic Regression, Random Forest, Decision Trees, and Support Vector Machine (SVM) - are implemented in Text classification.