Climate change poses significant challenges for developing economies, where environmental vulnerabilities often intersect with structural economic fragilities. This study investigates the relationship between climate-related stressors water scarcity, rising temperatures, and greenhouse gas emissions, and economic growth in Morocco, adopting an Artificial Intelligence (AI) perspective. Unlike traditional econometric models, which assume linearity and stationarity, we employ a Random Forest Regression approach to capture nonlinear interactions and complex dependencies across variables. Using annual data from 1990 to 2022, sourced primarily from the World Bank and national statistical reports, the model demonstrates strong predictive accuracy, with an R2 of 0.94 and low error metrics. The results highlight water stress as the most influential determinant of Morocco’s economic trajectory, accounting for 41% of the variance in growth outcomes, followed by temperature increases (34%) and greenhouse gas emissions (25%). While water scarcity and higher temperatures exert negative impacts on GDP growth, greenhouse gas emissions are found to contribute positively in the short term, reflecting the country’s reliance on carbon-intensive growth pathways. These findings underscore the dual challenge facing Morocco: managing immediate growth pressures while navigating a necessary transition toward sustainable and resilient development models. By demonstrating the added value of AI methods in climate–economy analysis, this research contributes both methodologically and substantively to ongoing debates on adaptive strategies for climate-resilient growth in emerging economies.