Increased energy consumption is the primary critical challenge within cluster computing environments, especially at the time data centers expand computational capabilities. Generally, such expansions have increased impacts on the environment, and higher operation costs are always associated with increased growth. Optimizing techniques toward energy-efficient cluster computing are research topics that fully analyze workload distribution, resource allocation strategies, and management of power distribution. Having the experimental results of gathering data from 200 nodes for a cluster spread out over 18 months, proposed and validated an adaptive approach for resource management; these adjustments depended on workload patterns and energy consumption metrics. Our method thus resulted in decreasing energy by up to 42% while sustaining performance at about 95% as that of the baseline. The system decreased peak power demands by 38% during periods of high utilization while increasing CPU utilization by 27%. The results seem to indicate that considerable energy savings in cluster computing environments are possible with minimal performance degradation, thus offering a practical route for data centers to upgrade their sustainability practice with efficient operations.