This study examines the increasing role of artificial intelligence (AI) in Costa Rica’s agricultural sector, emphasizing its potential to enhance sustainability, resource management, and market competitiveness. Using a mixed-methods approach, the research integrates structural equation modeling (SEM), multivariate regression analysis, text mining, and scenario analysis to provide a comprehensive evaluation of AI adoption. AI-driven solutions optimize key agricultural processes, including climate pattern prediction, soil condition monitoring, crop disease detection, and pest management. Quantitative findings indicate a strong correlation between AI adoption and improved productivity, economic benefits, and environmental conservation, particularly through optimized fertilizer and pesticide use and enhanced water management. However, challenges such as high implementation costs, limited d...