The present research investigates optimizing energy-efficient computing environments through dynamic resource allocation in edge computing settings. The primary objective is to enhance system efficiency and energy economic performance. A comprehensive data gathering and analysis plan, incorporating simulation, has been designed to gain insights into power usage patterns. Specifically, smart meter data from Bareilly for the years 2020 and 2021 will be examined to identify hourly and seasonal fluctuations in power consumption. The analysis framework supports applications such as predictive resource scaling and adaptive load balancing, which dynamically allocate resources in real time based on demand. The evaluation criteria include resilience, scalability, system performance, and energy efficiency concerning system usage. The key findings of this study contribute to the development of efficient resource allocation strategies aimed at improving energy management in edge computing environments and addressing practical concerns in energy consumption and performance optimization.