The swift proliferation of edge devices in large-scale systems poses challenges in resource utilization and data management, with limited processing power, memory, and bandwidth, especially for resource-intensive join operations. While Edge Query Processing (EQP) accelerates hash joins using equi-height binning, it is inefficient for real-time processing and scans unnecessary cachelines. This paper introduces Energy-Efficient Edge Query Processing (EEEQP), which optimizes hash joins by predicting query workloads in advance, making it ideal for real-time edge query processing. EEEQP has three modules Pre-processing, Prediction, and Blocking and Hashing, which compute bins based on predicted workloads. It also handles query execution and optimizes CPU, RAM, and I/O usage. Evaluations using a Benchmark dataset and a Real-World dataset from the Smart City domain demonstrate significant improvements in the Percentage of Cachelines Read PCR, Query Execution Time QET, and Resource Utilization. Real-world PCR and QET show 49% and 8% improvement, respectively. The proposed technique has been demonstrated to work well for scaled and skewed data. Although PCR is an indirect measure of energy consumption, the presented work also measures energy consumption directly by performing energy-efficiency experiments. EEEQP shows 2%, 38%, and 46% gain in CPU, RAM, and I/O, respectively. This enhances system performance and supports sustainable practices in database management, making EEEQP a promising choice for resource-constrained edge applications.