As we delve into the transformative impact of artificial intelligence (AI) on various sectors, one of the pressing issues that have surfaced is its energy consumption. The exponential growth of AI technologies and their applications has led to a significant increase in energy demand, making energy efficiency a priority for researchers and developers alike. AI models, particularly large language models and deep learning frameworks, require substantial computational power. This, in turn, leads to increased energy consumption, creating a ripple effect on the environment and operational costs. The AI community is increasingly aware of these challenges and is exploring solutions to mitigate the impact. Developers are now focusing on optimizing algorithms to ensure that models can perform efficiently without compromising on power. Additionally, there’s an emphasis on AI hardware advancements to support energy-efficient processing. Innovations like neuromorphic computing, which mimics the human brain, offer promising pathways for reducing energy usage. Furthermore, the concept of edge AI is gaining traction, where data processing is moved closer to the data source, thus reducing the energy required for data transmission and large-scale computations. Another pivotal approach is leveraging federated learning. By decentralizing data training across multiple devices, it minimizes the necessity for extensive computational resources in a central server, thus conserving energy. This not only addresses data privacy concerns but also promotes energy efficiency. It is evident that while AI continues to revolutionize industries, its sustainability hinges on how effectively we address its energy demands. Collaborative efforts in research and technological advancements will prove crucial in steering AI towards a more sustainable future.