The importance of academic research in solving problems and facilitating growth and development cannot be underestimated. When conducting academic research, one of the significant problems is knowing the recently published papers and getting related research papers to use for review and citation. This project develops a top-k document ranking and sentence similarity retrieval system for covid-19 research papers. The work is subdivided into top-k document ranking and sentence similarity retrieval. The former helps researchers working on Covid-19 related research to retrieve highly relevant research papers and articles related to Covid-19 topics based on their preferences by generating inverted indices using TF-IDF. It also searches and retrieves the best match and sorted list of most relevant papers and articles using Okapi BM25. The latter help to retrieve the top-k sentences, which can help researchers get sentences for citation in literature reviews. It uses TS-SS to get similar sentence(s) to the user query from the highly relevant document by improving the query answering accuracy and less time complexity. Manual evaluation with user queries was used to test and evaluate the models. The result in this work shows an example of document retrieval of titles of the top-5 relevant research papers from the collection of Covid-19 and coronavirus-related documents was ranked and retrieved to the user query. For sentence retrieval, the experiment shows an example of the top-4 similar sentence(s) for the highly relevant article, which was evaluated by utilizing a user query to search from the document. The TS-SS model successfully retrieves the most related sentences from the document using the weighted maximum and indexed similarity.