This comprehensive review explores the transformative impact of deep learning technologies on the analysis, selection, and detection of soybean seeds. By leveraging state-of-the-art deep learning architectures, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), this article highlights advancements in seed grade assessment, viability detection, and yield prediction. The review emphasizes the integration of real and historical agricultural data to enhance model accuracy and performance. Key parameters such as seed health, soil conditions, and weather patterns are discussed, alongside challenges in data availability, computational resource requirements, and model generalization. The study focuses on the specific context of soybean farming in Maharashtra, aiming to provide actionable insights for farmers and stakeholders. Finally, the paper identifies future research directions, emphasizing the need for user-friendly and cost-effective AI solutions for small-scale farmers. The review highlights the integration of CNNs, GANs, and Recurrent Neural Network (RNN) for soybean seed classification, emphasizing their application in creating robust, annotated datasets and handling real-world challenges like data variability and scarcity. It bridges agricultural needs with AI-driven solutions, offering insights for precision farming.