Artificial intelligence (AI) is reshaping smart agriculture through prediction, automation, resource optimization, and data-driven decision-making. However, its effective and sustainable deployment depends on reliable Internet of Things (IoT) data infrastructures supporting sensing, connectivity, data acquisition, transmission, and distributed processing. This review maps research at the intersection of AI, IoT, and smart agriculture and examines its data-infrastructure requirements and sustainability implications. Drawing on 441 Scopus-indexed, peer-reviewed journal articles published between 2017 and 2025, the study combines bibliometric performance analysis, science mapping, and content-based thematic interpretation to identify publication trends, intellectual foundations, influential research fronts, emerging themes, and deployment challenges. The bibliometric analyses characterize AI–IoT research in smart agriculture, while IoT data infrastructure provides the principal analytical lens, and sustainability and responsible deployment are examined through interpretive synthesis. The findings reveal rapid publication growth and prominent themes involving connected sensing, predictive analytics, edge intelligence, remote sensing, smart irrigation, cybersecurity, blockchain, and edge–cloud processing. Persistent barriers include limited interoperability, inconsistent data quality, energy requirements, scalability constraints, cybersecurity and governance risks, affordability, and unequal adoption in resource-constrained contexts. The review concludes that sustainable AI-enabled agriculture requires reliable, secure, interoperable, energy-efficient, inclusive, and context-appropriate digital infrastructures.