Smart farming uses machine learning to analyze the behavior of climate change over crops to forecast crop yield in recent years, Machine learning and RGB UAV-based imagery are used in the agriculture field for crop yield prediction. Now a day’s remote sensing is used for crop yield prediction and analyzing the nutrients of the crop. Various machine learning algorithms such as Random Forest, CNN, and DNN are used for crop yield prediction. A systematic literature review is conducted to analyze which machine learning algorithms are used with remote sensing for crop yield prediction studies. In this study, 487 papers are extracted from various databases and selected 50 papers for based on inclusion and exclusion criteria. Analysis of these 50 papers shows that most of the studies are conducted for crop yield prediction and to investigate the nutrients of crops. In these studies, various machine learning algorithms are used in remote sensing of crops through UAV and also investigate how remote sensing through UAV help full for monitoring and balancing the fertilizer needs of a crop.