Crop yield prediction is a critical aspect of agricultural research, with implications for decision-making in areas such as import-export, pricing, and distribution of specific crops. Machine learning algorithms have emerged as effective instruments for enhancing the accuracy of crop yield prediction models in recent years. This article presents a state-of-the-art overview of smart agriculture and the latest machine learning algorithms used for crop yield prediction with emphasis on European Union. The article concludes by highlighting the significance of machine learning algorithms in precision crop management systems and their potential to revolutionize agricultural decision-making. Overall, this comprehensive review provides valuable insights into the latest advancements in machine learning algorithms for crop yield prediction, offering a foundation for future research and practical applications in agriculture.