Global agriculture faces growing challenges from population growth, climate change, and resource scarcity, demanding innovative solutions for food security and sustainability. Artificial Intelligence (AI), combined with machine learning and IoT, is transforming crop production through precision agriculture. This review examines AI applications in soil evaluation, crop monitoring, pest and disease management, and resource optimization, based on a systematic analysis of recent literature from major databases (Google Scholar, PubMed, Scopus, Web of Science). By integrating robotic automation and sensor-based analysis, AI boosts productivity, reduces resource waste, and supports sustainable practices. Despite barriers such as high costs and limited accessibility, recent AI advancements offer strong potential to enhance global food security. The paper recommends developing affordable, scalable solutions, open-access datasets, lightweight models, and farmer-training programs to facilitate broader adoption. In Vietnam, AI can optimize irrigation and fertilizer use for key exported crops such as rice, coffee, and cashew, enable early pest detection via drones and sensors, and promote low-emission farming, thereby advancing sustainable agriculture while increasing productivity and reducing environmental impact.