Resource Management Challenges in AI-Driven Data Centers: A Systematic Review of Energy, Water, and Material Constraints

Abstract

The rapid expansion of artificial intelligence has intensified the demand for high-performance data centers, leading to unprecedented pressures on energy, water, and material resources. This critical review examines the emerging challenges associated with resource management in AI-driven data centers by focusing on the interplay between computational growth and environmental constraints. The analysis integrates recent advances in energy efficiency, cooling technologies, and hardware design while highlighting the increasing water footprint of thermal management systems and the material implications linked to semiconductor manufacturing and infrastructure scaling. Particular attention is given not only to the trade-offs between performance optimization and sustainability but also to the limitations of current metrics used to assess resource efficiency. The review identifies key gaps in the literature, including the lack of integrated frameworks that simultaneously address energy, water, and material flows. Finally, this review provides insights into pathways for a more sustainable AI infrastructure by synthesizing present-day knowledge, critically evaluating existing strategies, and emphasizing the need for systemic approaches that align technological innovation with resource conservation and long-term environmental resilience.