Classifying unstructured data is quite challenging as useful as it is. The overwhelming amount of textual data are collected in web, which when properly processed and categorized could open potential business opportunities. It has become significant to classify varieties of educative (books, poems, etc.,) and entertaining (movies, etc.,) information on web for recommendation systems to serve users better as well. With increasing innovations and breakthroughs in this domain, studying ‘(automatic) genre classification’ stands non-trivial for its invaluable applications in improving web search results, information retrieval, etc., The aim of this paper is to bring to light the notable trends in this domain and its several stages. The distinctive features of different methods, along with the datasets and evaluation metrics used are compared. The crucial role of classifying text according to their genres in wide applications has also been pointed out in addition to the challenges, to draw research attention in these nascent areas.