Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual information and language processing system can use this information for better decision support and quality of life care. Further, a learnt model can efficiently utilize written healthcare information in knowledgeable patterns. The word–document and document–document linkage can help in gaining better contextual information. We analyzed 124 research articles in text and healthcare domain related to the ML paradigm and showed the mechanism of intelligence to capture hidden insights from document representation where only a term or word is used to explain the phenomenon. Mostly in the research, document–word relations are identified while relations with other documents are ignored. This paper emphasizes text representations and its linage with ML, DL, and RL approaches, which is an important marker for intelligence segregation. Furthermore, we highlighted the advantages of ML and DL methods as powerful tools for automatic text classification tasks.