Energy-Efficient Edge Cloud Architectures Using AI and Renewable Energy Integration

Cloud computing adoption has remained soaring with high energy consumption that raises serious environmental issues. The existing techniques do not properly handle the inadequacies of intermittent renewable sources and inefficiencies in dynamic workload management, mainly for latency-sensitive and resource-intensive applications. Thus, this work presents an AI-driven energy management framework that combines renewable energy with edge computing to support sustainable cloud architectures. The purpose of this work is to optimize the distribution of workload, resource usage, and energy consumption while providing uninterrupted system performance. The novelty in this work comes from its hybrid AI approach in using deep reinforcement learning for adaptive energy allocation and sophisticated scheduling algorithms that mitigate energy intermittency. The experimentation was performed on a real-world cloud performance dataset from Kaggle, with comprehensive metrics. The future model had an accuracy of 94.6% for workload prediction and 87.5% for resource utilization, significantly outperforming baseline methods. Key findings include a 35.2% reduction in energy consumption and a latency improvement of 120 ms, demonstrating the framework's effectiveness in enhancing operational efficiency. These results will help understand where AI-integrated solutions can actually revolutionize energyefficient cloud computing, towards global sustainability goals, and scalable approaches for future digital ecosystems. The perspective of AI regarding energy-efficient cloud edges with regard to future developments seems quite stable. It doesn't really discuss the practical aspects of adapting to diverse environments or hybrid renewable integration. It doesn't address the issue of infrastructure diversity either. Hence, future research seriously needs to consider these areas to improve the scalability, reliability, and sustainability of models developed for real-world applications.