Efficiency improvements due to large-scale applications require tremendous computational resources, which increasingly emanate from colossal energy consumption and carbon emissions. This research investigates green prompt engineering as one of the mitigating strategies that may help reduce Cloud AI’s environmental cost. Green prompt engineering leverages how to optimize the prompt in AI tasks to have more computational processes with minimal resources or, in other words, without deterioration in model performance. This research points out that extensive cloud-based AI is resource-intensive, and large language models are judged on their environmental cost regarding the carbon footprint of data centers and electronic waste. It discusses green AI strategies in three major areas: prompt optimization, sustainable data management, and energy efficiency in cloud providers. Reviewing case studies and industrial applications, we emphasize how practical green prompt engineering reduces computational demand. Our results put into perspective the dire need for sustainability in AI practices, actionable guidelines for developers and cloud providers, and policy recommendations that can help further the cause of green AI. By implementing such strategies, the AI industry can continue to play a vital role in helping reduce its ecological footprint and contribute to a sustainable technological future.