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Rice is a major food crop for a significant population of the world and increase in its yield is a priority to ensure nutrition for many countries. Thus it is important to protect the crop from the various diseases that affect the crop. With the advent of machine learning, a number of methods have been proposed to identify diseases after the occurrence as well as at an early stage. Diagnose of these predators before occurrence can be much more beneficial. Journal papers that used rice as a major crop were considered eligible. In this review, an attempt is made to understand the preferred machine learning methods and algorithms that can be employed to detect various diseases and pest as well as predict crop yield of the rice crop. It is expected to be beneficial to all who wish to use machine learning in agriculture that can lead to constructive research in this area.