Agriculture remains the backbone of South Asia’s economy, yet persistent challenges such as climate variability, low productivity, and fragmented supply chains hinder its sustainable growth. Motivated by the region’s increasing digitalization and the potential of Artificial Intelligence (AI) to address these constraints, this study investigates how AI is being adopted in agriculture across six South Asian countries—Bangladesh, India, Pakistan, Sri Lanka, Nepal, and Afghanistan. Using a qualitative comparative case study approach, the research integrates the Technology Acceptance Model (TAM) and the Diffusion of Innovation (DOI) framework to analyze over 30 AI-enabled firms operating in areas such as farm finance, crop monitoring, pest and disease management, logistics, and post-harvest quality control. The findings reveal significant cross-country variation in AI adoption patterns: India and Sri Lanka exhibit DOI-driven, institution-led diffusion supported by strong public–private partnerships, while Bangladesh and Nepal rely on TAM-based, individual-level acceptance influenced by digital literacy and perceived ease of use. Pakistan demonstrates a hybrid adoption path, whereas Afghanistan remains at an early diffusion stage. These results highlight that infrastructure readiness, digital capability, and institutional coordination are decisive factors for scaling AI in agriculture. The study concludes with actionable policy recommendations, emphasizing the need for region-wide investment in rural digital infrastructure, AI literacy programs, inclusive financing mechanisms, and cross-border public–private collaborations to accelerate sustainable, data-driven agricultural transformation across South Asia.