Machine learning plays an emerging role in agricultural systems to further enhance crop production and efficiently manage water and soil resources. For example, machine learning algorithms are applied to crop management, water management and smart irrigation systems, and soil management. Recently, climate change has significant impacts on agriculture. In particular, the climate change influences numerous agricultural aspects including soil properties, fertilization, irrigation mechanisms, and crop yield. Specifically, climate change has substantial impacts on crop production and food security. Thus, this work focused on the impacts of climate change on crop yield. In particular, this work essentially reviews the main machine learning approaches employed for studying and analyzing the impacts of the climate change on crop yield prediction.