The swift development of digital infrastructures has increased sustainability and operational pressure on data centers globally. Data centers, which are the foundation of modern smart cities, require creative approaches to manage energy use while preserving security and reliability. The goal of this project is to provide an intelligent framework for sustainable data center operations that are mostly driven by renewable energy. This research work focuses on data analysis and AI-driven forecasting models to optimize energy management in data centers, aligning with the concepts of operations management through resource efficiency process optimization. The study makes use of renewable energy data that was gathered from NSRDB website, over a ten-year period (2014–2024) from 20 cities in the United States. The datasets are analyzed in order to explore solar irradiance patterns and evaluate the feasibility of incorporating renewable energy sources into data center operations. Forecasting Global Horizontal Irradiance (GHI), a crucial indicator of solar energy availability, is the main objective. The approach merges manual preprocessing with automated feature selection technique, which is Mutual Information and XGboost, to guarantee data relevance and model performance. With the aim of forecasting GHI and assisting data driven decisions on the location and timing of the establishment of energy-efficient data centers, the research adopts both machine learning and deep learning techniques models (such as Random Forest, Long Short-Term Memory). From an operations management perspective, this project supports sustainable data center operations by facilitating predictive energy allocation, reducing reliance on non-renewable resources, and enhancing long-term operational planning. Based on the potential for renewable energy, the generated models are used as decision-support for optimizing site selection and operational scheduling. In conclusion, this study emphasizes the importance of AI algorithms in improving data centers’ sustainability and operational efficiency. By including renewable energy predictions into data-driven decision-making, the project shows how advanced analytics may direct the best possible operation of adaptive data centers that support sustainability and energy efficiency objectives. The results demonstrate the potential of industrial engineering and operations management techniques in balancing technological excellence, environmental responsibility and strategic resource utilization, opening the door to a more intelligent and greener digital future.