Optimizing Cloud Energy Use With Artificial Intelligence and Machine Learning
Ashish Semwal, ManMohan Singh Rauthan, Varun Barthwal · Cureus Journal of Computer Science. · 2026
The rapid growth of cloud computing services has intensified the challenge of energy optimization in data centers, raising pressing environmental concerns. Machine learning (ML) and artificial intelligence (AI) offer powerful solutions for enhancing energy efficiency across cloud infrastructures. This paper reviews key AI- and ML-driven approaches - including energy-aware resource provisioning, adaptive scaling, predictive maintenance, intelligent job scheduling, and advanced cooling optimization - that collectively minimize energy use. These models not only detect and correct power consumption anomalies but also balance workloads to reduce waste. Furthermore, integrating energy-sensitive algorithms and leveraging edge computing can significantly cut the environmental footprint of cloud operations. Ultimately, by improving operational efficiency and lowering energy costs, AI and ML are vital to advancing sustainable, energy-efficient cloud computing.