This research hypothesizes that the integration of AI and IoT technologies including WSNs, GPS/GIS, deep learning, and machine vision within smart agriculture systems such as greenhouses and digital crop advisories can significantly improve input efficiency, reduce environmental footprints, and increase smallholder inclusivity. It further posits that enabling technologies like 5G/6G, edge computing, multispectral imaging, and blockchain-enabled recycling will enhance real-time decision-making, support autonomous operations in remote terrains, and mitigate lifecycle impacts of agricultural digitization. Collectively, these advancements are expected to contribute to Sustainable Development Goals (SDGs) 2 (Zero Hunger) and 13 (Climate Action). A systematic literature review (PRISMA protocol; conducted Jan- May 2025) analyzed 85 peer-reviewed studies (2020–2025) from Scopus, Web of Science, and IEEE Xplore, employing thematic assessment of technical efficacy, socio-economic adoption, environmental trade-offs, and policy frameworks. AI-controlled greenhouses achieved 40% water savings in arid regions via precision irrigation; digital advisories with VRT reduced orchard pesticide use by 55%; voice-based NLP alerts boosted smallholder engagement by 89%; solar-edge computing lowered emissions by 35%; and blockchain-driven recycling achieved 85% sensor reuse. Critical barriers included LiDAR signal limitations under dense canopies, interoperability gaps between legacy/modern systems, and high costs excluding 60% of smallholders.