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Agriculture is the third most contributing sector in the Indian Gross Domestic Product (GDP). Selecting right crop for the right soil at the right season plays a major role in getting good crop yield. A vast majority of the farmers believe on their intuition to grow crops in a particular season based on which they might incur losses. Based on the segregation of huge amount of data using Machine Learning (ML) algorithms, the important decisions like prediction of crop yield, for the type of soil or season can be taken. In this study, comparative literature review is carried out, giving ideas about various algorithms, their features which are popularly used in crop prediction. According to research, temperature and soil type are the most important parameters. Deep Neural Networks (DNN) is one of the most commonly used algorithms.