Agriculture is always being considered as an important sector that controls the economy of any nation. This sector mostly controls GDP growth. But this sector most of the times suffer from many uncontrollable factors that affect its growth. Before the start of any new crop farming a farmer needs to inspect each and every aspect of the farm. The cultivation of different types of crops has diverse needs in terms of farm conditions and other parameters to improve crop yield. Farmers do not achieve crop yields as per their expectations due to insufficient knowledge about the farming requirements of different crops. Many villages lack sufficient agriculture officers. As a result, it is not always possible for each agriculture officer to visit every farmer’s field. This limits their capability to deliver personalized guidance based on the specific needs of each farm. For this problem, asrtificial intelligence can be utilized as a useful tool to facilitate a farmer in farming. This paper presents a detailed investigation of various machine learning methods proposed by different researchers used for crop yield prediction.