Energy-Aware Machine Learning Frameworks for Sustainable Intelligent Computing in Large-Scale Systems

With the advent of big-scale smart computing, computational loads are growing exponentially, which has posed a danger to sustainability and scalability due to an increase in energy consumption. To solve this problem, the present paper proposes an energy-aware machine learning (ML) framework that can optimize its performance and reduce its power consumption in the distributed context. The framework incorporates deep learning (DL) models with energy-conscious scheduling and model pruning based on the heterogeneous datasets, such as CPU usage, memory usage, network usage, and system-energy information. The proposed system has adaptive learning mechanisms, unlike traditional approaches, which focus on the accuracy of predictions with no attention to the overhead of the resource allocation, which dynamically re-calibrates resource allocation according to the variations in the workload, enhancing the efficiency and resilience of the system. The algorithm is a hybrid CNN-LSTM workload prediction model with Transformer-based models to address long-term relations and use energy indicators in decision-making cycles. System performance-measured in predictive accuracy, decision latency, energy efficiency and sustainability index is mathematically modeled and optimized in the framework. Comparison of simulation-based predictive control proves the proposed approach to be 14.8 percent more predictive control-wise accurate, 27 percent energy consumption-wise less, and 19 percent latency-wise lesser than baselines like the Random Forest and standard LSTM models. Moreover, the stress tests at the peak loads and system volatility verify that the framework maintains a high level of adaptability, and the traditional approaches decline considerably. The proposed system illustrates how the energy-conscious ML can transform how decisions are supported through energy-efficient and accurate and scalable decision support. This study is a foundation of sustainable intelligent computation that represents the future of large-scale computing systems with an appropriate balance between performance and environmental responsibility.