The rapid advancement of artificial intelligence (AI) presents transformative opportu- nities for sustainable development, particularly in shaping environmental outcomes such as renewable energy consumption and carbon dioxide (CO2) emissions. This study in- vestigates the empirical relationship between AI research activity and key environmental metrics across multiple countries from 2010 to 2025, leveraging panel data from the World Development Indicators (WDI) and bibliometric records from Scopus. Utilizing robust data science methods—including advanced preprocessing, machine learning-driven forecasting, panel regressions, and multivariate clustering—we analyze how the intensity of AI-related publications correlates with national renewable energy usage and CO2 emissions. Our inter- active Shiny dashboard empowers stakeholders to dynamically explore trends, model results, and predictive scenarios. Results reveal a statistically significant but modest positive effect of AI research output on renewable energy adoption, highlighting both the promise and the limitations of technological drivers in global sustainability transitions. The paper addresses methodological challenges, biases in research coverage, and future directions for integrating policy indicators and advanced models to deepen insights. This work contributes to the growing body of research at the intersection of AI and sustainable development, offering actionable findings for researchers, policymakers, and the wider community.