Optimizing AI Training Sustainability Through Renewable Energy Integration in Data Centers

The rapid progress of what is referred to as Artificial Intelligence (AI) has suggested a rise in the power required in the computational Soap Fires, mainly to greater “Energy Consumption” (EC) and higher “Carbon Emissions” (CE) levels in the so-called Data Centers (DCs). Due to the larger resource requirement for training AI models, environmental sustainability has now become a major concern. Renewable Energy (RE) and Carbon Footprint (CF) monitoring are examined in this paper as tools to market the sustainable AIdriven DC. Mixed method has been chosen, wherein a mixture of structured surveys of 121 professionals was employed along with secondary data from the academic literature, industry reports, and case studies. The analysis carried out with surveydriven data and statistical tools, including descriptive analysis, reliability testing, and ordinal regression, is a research effort that evaluates the effectiveness of$\mathbf{R E}$adoption and compares it with other sustainability metrics. The results indicate that even though there is a consistent idea of RE integration with green AI efforts, its impact on the decrease of CE and EC is not statistically significant. Still, the results highlight the crucial role of carbon monitoring systems and Energy-Efficient (EE) practices as the principal enablers of the sustainable AI ecosystem. The research emphasizes the necessity of multifaceted strategies, increased policy support, and long-term investment in the case of the implementation of the environmentally aware AI development.