This comparative review explores the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on developments in the United States (USA) and Africa. AI technologies have significantly transformed agriculture globally, addressing challenges and improving traditional farming practices. In the USA, precision agriculture, drone technology for crop monitoring, and predictive analytics for yield optimization are key innovations. These technologies enable efficient resource management, reduce environmental impact, and enhance productivity. In contrast, AI in African agriculture faces challenges such as limited infrastructure, resource constraints, and the need for context-specific solutions. Initiatives in Africa focus on pest and disease detection, crop monitoring in remote areas, and data-driven decision-making tools for smallholder farmers. The review highlights disparities in AI adoption between the USA and Africa, emphasizing factors like infrastructure, technological accessibility, and resource availability. Collaborative efforts and partnerships are crucial for bridging the gap and promoting sustainable AI development in African agriculture. The findings underscore the need for tailored approaches, policy frameworks, and international collaborations to ensure inclusive and equitable access to AI-driven innovations in agriculture, fostering sustainable and technologically empowered farming practices.This comparative review explores the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on developments in the United States (USA) and Africa. AI technologies have significantly transformed agriculture globally, addressing challenges and improving traditional farming practices. In the USA, precision agriculture, drone technology for crop monitoring, and predictive analytics for yield optimization are key innovations. These technologies enable efficient resource management, reduce environmental impact, and enhance productivity. In contrast, AI in African agriculture faces challenges such as limited infrastructure, resource constraints, and the need for context-specific solutions. Initiatives in Africa focus on pest and disease detection, crop monitoring in remote areas, and data-driven decision-making tools for smallholder farmers. The review highlights disparities in AI adoption between the USA and Africa, emphasizing factors like infrastructure, technological accessibility, and resource availability. Collaborative efforts and partnerships are crucial for bridging the gap and promoting sustainable AI development in African agriculture. The findings underscore the need for tailored approaches, policy frameworks, and international collaborations to ensure inclusive and equitable access to AI-driven innovations in agriculture, fostering sustainable and technologically empowered farming practices.