In the era of sustainable technology, energy-efficient green data centers are critical for minimizing environmental impact while maintaining high-performance computing capabilities. This paper explores the integration of Machine Learning (ML) and Internet of Things (IoT) technologies to optimize load forecasting in these data centers. By leveraging advanced ML algorithms and IoT sensors, we aim to predict computational load with high accuracy, enabling dynamic resource allocation and energy optimization. This approach not only enhances operational efficiency but also significantly reduces energy consumption and carbon footprint. Our results demonstrate that incorporating ML and IoT for load forecasting improves predictive accuracy and facilitates more sustainable data center operations. This paper provides valuable insights into the practical application of these technologies, offering a blueprint for future advancements in green data center management.