The rapid expansion of cloud computing has transformed modern digital systems by enabling scalable, flexible, and high-performance computing services. Organizations across sectors increasingly rely on cloud platforms to enhance productivity, support innovation, and meet growing computational demands. However, this widespread adoption has also led to a sharp increase in energy consumption and carbon emissions. Data centers, which serve as the foundation of cloud infrastructure, are among the most energy-intensive facilities, making them significant contributors to environmental degradation. Projections suggest that by 2030, data centers in the United States alone may account for nearly 12% of total electricity consumption, highlighting the urgent need for sustainable cloud solutions. This research presents a sustainable green cloud computing framework aimed at improving energy efficiency and reducing carbon emissions without compromising system performance and scalability. The proposed approach combines intelligent workload management, dynamic resource allocation, energy-aware task scheduling, and adaptive cooling strategies to lower overall power consumption in cloud environments. By using predictive models and real-time monitoring, the framework enables effective balancing of computational workloads and energy usage, resulting in improved operational efficiency and reduced environmental impact. Comprehensive experimental evaluation using real-world cloud workloads demonstrates that the proposed framework achieves up to 35–45% reduction in energy consumption and 30–40% decrease in carbon emissions, while maintaining reliable service performance. These results confirm the effectiveness of sustainable optimization techniques in developing environmentally responsible cloud infrastructures. This study contributes a practical, scalable, and eco-friendly solution, offering a viable pathway toward carbonneutral and sustainable cloud computing systems capable of supporting future high-performance applications.