Eco-conscious green AI: Reviewing sustainability approaches to minimize environmental impact

The rapid development in artificial intelligence (AI) has led to a surge in computational demands, raising significant concerns about its environmental impact. This review synthesizes recent research on the sustainability of AI systems, focusing on energy efficiency and environmental considerations starting from training to deployment which covers the entire AI lifecycle. It evaluates innovative approaches for monitoring and reducing carbon emissions, including lifecycle assessments, carbon trackers, and machine learning (ML) emission calculators. Key strategies for energy-efficient AI discussed include hardware-software co-design, algorithmic optimizations, and data-centric methodologies that balance energy consumption with model performance. This study also underscores the importance of transparency in reporting energy metrics and advocates for standardizing such practices across research and industry. By analysing current challenges and emerging solutions, the paper highlights actionable pathways toward achieving Green AI, offering practical recommendations for researchers, developers, and policymakers. This review aims to promote a paradigm shift toward sustainable AI development by aligning technological innovation with environmental responsibility.