This study investigates the impact of climate change on grapevine phenology in the Côte de Beaune area by analyzing historical climatic data and phenological events of the Pinot Noir variety. Climate change has significantly affected phenological stages, especially bringing forward critical stages which directly influence grape quality and composition. We employed ordinary least squares (OLS) regression models and advanced artificial intelligence (AI) models, including Random Forest, to model the nonlinear relationships between climatic variables and phenological stages. The results show that Random Forest outperformed OLS regression, achieving higher accuracy in predicting phenological events. These results highlight the importance of AI-based models for predicting the effects of climate change on viticulture and developing strategies to mitigate its impact on wine production.