A systematic review of Green Machine Learning: practices and challenges for sustainability

Abstract The growing energy demands and environmental impact of Machine Learning (ML) models have established Green Machine Learning (GML) as an important field for sustainable AI progress. This systematic review incorporates recent advances in energy-aware ML, contributing an overall assessment of algorithmic strategies, energy-aware design, and sustainability practices that reduce carbon footprints while preserving performance. The review identifies five basic dimensions of GML: algorithmic efficiency, energy optimization, hardware efficiency, carbon footprint tracking, and green benchmarks. Key findings indicate that pruning, quantization, and low-precision training can reduce energy consumption without significant loss of accuracy. Energy-aware federated learning and lightweight architectures have demonstrated potential savings in distributed environments. In contrast to prior work, this review integrates a full ML life-cycle perspective and contextualizes technical methods within broader sustainability frameworks, including the UN Sustainable Development Goals. It also surveys emerging trends, including energy-aware Federated Learning, Automated ML under $$\text{CO}_2$$ constraints, and low-power inference on edge devices. Critical challenges remain, including the lack of standardized reporting metrics, barriers to reproducibility, and the trade-off between energy savings and predictive accuracy. This review offers structured research opportunities to support the development of GML across various domains. It serves as a reference for researchers and practitioners committed to integrating environmental responsibility into ML innovation.