This comparative review examines the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on the United States (USA) and Africa. In the USA, AI has significantly transformed precision agriculture, smart farming techniques, autonomous machinery, drone technology for crop monitoring, and predictive analytics for yield optimization. These innovations have enhanced efficiency and sustainability in modern agricultural systems. In contrast, African agriculture faces unique challenges such as limited infrastructure, resource constraints, and the prevalence of smallholder farming. AI applications in Africa include pest and disease detection, remote crop monitoring, and data-driven decision-making tools for smallholder farmers. These initiatives aim to improve productivity, resource management, and food security. The review highlights the disparities in AI adoption between the USA and Africa, emphasizing factors like infrastructure, technological accessibility, and resource availability. Collaborative efforts and partnerships are crucial to bridge these gaps, ensuring inclusive and equitable access to AI-driven innovations. The future outlook for AI in agriculture emphasizes the need for tailored approaches, robust policy frameworks, and international collaborations to address global food security and environmental conservation.This comparative review examines the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on the United States (USA) and Africa. In the USA, AI has significantly transformed precision agriculture, smart farming techniques, autonomous machinery, drone technology for crop monitoring, and predictive analytics for yield optimization. These innovations have enhanced efficiency and sustainability in modern agricultural systems. In contrast, African agriculture faces unique challenges such as limited infrastructure, resource constraints, and the prevalence of smallholder farming. AI applications in Africa include pest and disease detection, remote crop monitoring, and data-driven decision-making tools for smallholder farmers. These initiatives aim to improve productivity, resource management, and food security. The review highlights the disparities in AI adoption between the USA and Africa, emphasizing factors like infrastructure, technological accessibility, and resource availability. Collaborative efforts and partnerships are crucial to bridge these gaps, ensuring inclusive and equitable access to AI-driven innovations. The future outlook for AI in agriculture emphasizes the need for tailored approaches, robust policy frameworks, and international collaborations to address global food security and environmental conservation.