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Large-scale deep learning workloads increasingly suffer from I/O bottlenecks as datasets grow beyond local storage capacities and GPU compute outpaces network and disk latencies. While recent systems optimize data-loading time, they overlook the energy cost of I/O—a critical factor at large scale. We introduce EMLIO, an Efficient Machine Learning I/O service that jointly minimizes end-to-end data-loading latency (𝑇) and I/O energy consumption (𝐸) across variable-latency networked storage. EMLIO deploys a lightweight data-serving daemon on storage nodes that serializes and batches raw samples, streams them over TCP with out-of-order prefetching, and integrates seamlessly with GPU-accelerated (NVIDIA DALI) pre-processing on the client side. In exhaustive evaluations over local disk, LAN (0.05 ms & 10 ms round trip time (RTT)), and WAN (30 ms RTT) environments, EMLIO delivers on average up to 8.6X faster I/O and 10.9X lower energy use compared to state-of-the-art loaders, while maintaining constant performance and energy profiles irrespective of network distance. EMLIO’s service-based architecture offers a scalable blueprint for energy-aware I/O in next-generation AI clouds.