Since its inception in 1960, text classification has had a long history. Since then, Text classification has benefited much from the study. Some studies have modified the original tools, while others have incorporated innovative ideas in one or more steps of the text classification approach, but no general model has yet been proposed that can efficiently classify the text in multiple languages. This research examines the literature in twenty languages to determine what technical challenges exist that prevent the creation of a generic model. It also seeks to determine whether it is possible to develop a model that can classify text in many languages. Our findings show that The phases of representation and pre-processing are found to bring particular challenges for each language. Depending on the availability of the data and the precise classification objective, academics classify text published in languages other than English using essentially the same basic methodology, tools, and other components. There is no single Generic model that is acceptable across all languages, despite the adoption of comparable models in text classification across several languages. The questions posed as a research challenge for this study are addressed in this paper. Our findings will aid scholars in comprehending the problems encountered at various stages of the text classification process. It will also assist them in thinking of innovative ways such as reducing existing bottlenecks and opening up new study fields