Edge computing brings computation closer to data sources, reducing latency but posing new energy challenges for battery-powered and distributed resources. In this work, we present a machine learning (ML)-based framework that dynamically optimizes energy use in heterogeneous edge computing environments. Our approach incorporates adaptive workload distribution, hardware-aware ML model design, and low-power inference techniques. The framework uses learning algorithms (e.g. reinforcement learning) to allocate tasks across edge nodes and cloud resources based on current load and energy profiles. We evaluate the framework in a representative IoT scenario, showing that ML-guided scheduling can reduce energy consumption by ~30–50% compared to static allocation strategies while meeting latency constraints. Key innovations include multi-objective optimization of computational and hardware parameters (inspired by DynaSplit) and the use of model compression and accelerators to enable low-power inference. These results demonstrate the promise of ML-driven resource management for sustainable edge computing.
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