Digital twin technology is transforming the agriculture industry by designing virtual versions of real-life farming systems that can optimize farming operations through real-time monitoring and decision-making. This chapter provides a detailed discussion on the implementation of digital twins with the assistance of artificial intelligence (AI) and the Internet of Things (IoT), in the agricultural sector. The building block integrates IoT sensor networks to gather precise data, cloud and edge computing to compute data in real time, and AI/machine learning models to derive predictive analytics. The most important aspects are extremely precise soil moisture sensors (98%), environmental tracking tools, and livestock health gadgets. Cyber-security concerns, sensor deployments, data integration tools, and standardization are also the issues addressed in the chapter. According to the latest studies, the potential benefits are significant: AI irrigation can reduce water use by 18–25% and crop yields by 11–20%, detect pests more accurately than 92%, and accurate fertilizers can lead to a 25–30% reduction in resources. Problems such as technical complexity, economic barriers, and resistance to adoption are resolved with suggestions on how they can be removed. The future perspective shows the introduction of AI, 5G, and quantum computing to support sustainable change in agriculture.