Soybeans (Glycine max L.) have emerged as a pivotal global food crop, prominently recognized for their high protein content and versatile applications.Thus, learning in soybean crops, delves into the state-of-the-art methodologies in machine learning (ML) and artificial intelligence (AI) applied to soybean grading. This review synthesizes literature and studies from 2018 to 2023, drawing from diverse online publications. Results highlighted Random Forest (RF) and Support Vector Machines (SVM) as the most frequently employed ML algorithms in soybean grading. The review underscores the achievement of high-accuracy soybean classification with RF and SVM. While the literature review successfully synthesizes existing knowledge, it emphasizes the untapped potential of AI in agriculture.