Artificial Intelligence (AI) is rapidly transforming agriculture, offering advanced solutions to the long-standing challenges of climate variability, resource optimization, and food insecurity. However, in many parts of the Global South, particularly South Asia and Sub-Saharan Africa, the adoption of AI in climate-smart agriculture (CSA) remains in its infancy due to infrastructure, policy, and capacity barriers. This study develops a comprehensive roadmap for integrating AI into CSA decision systems in data-scarce and climate-vulnerable regions. This roadmap is formulated through a systematic meta-synthesis of over 200 peer-reviewed articles, FAO and World Bank reports, and real-world case studies of AI applications in agriculture. AI models, including machine learning, deep learning, and geospatial decision support systems, are critically analyzed in terms of their current utilization for yield forecasting, pest detection, early warnings of drought, precision irrigation, and digital farm advisory platforms. A technology–policy–capacity framework is proposed, illustrating how scalable AI tools can be embedded within national agricultural extension systems and local farmer knowledge networks. Therefore, the key findings highlight the potential of open-access satellite datasets (e.g., NASA POWER, Copernicus), federated learning for data privacy in rural areas, and low-power AI devices suited to resource-constrained environments. Ethical concerns such as algorithmic bias, digital exclusion, and governance vacuums are also addressed, with mitigation strategies proposed to ensure equitable AI deployment. This conceptual contribution offers a forward-looking strategy for aligning AI innovation with CSA goals, enhancing agricultural productivity, sustainability, and resilience. The proposed roadmap serves as a practical guide for researchers, policymakers, and agri-tech innovators committed to transforming agri-food systems across the Global South.