Millimeter-wave (mmWave) communication relies on directional beam sweeping for initial access, which incurs substantial latency and signaling overhead as antenna arrays grow large and deployments become denser. Machine-learning-based beam prediction can reduce this overhead, but its effectiveness depends on training data that accurately reflects the deployment environment, a requirement that is difficult to meet in practice. This paper presents a latency-aware beam management architecture that integrates digital-twin-assisted prediction, regionbased localization, weighted ensemble learning, and explainability-driven beam pruning into a unified framework. A digital twin (DT) generates site-specific ray-traced channel knowledge offline, enabling environment-aware beam prediction without exhaustive real-world measurements and supporting both initial access and beam tracking. The proposed ensemble learning scheme combines a base classifier and a specialist classifier through reliability-weighted fusion to improve prediction accuracy and robustness, while explainable artificial intelligence (XAI)-based attribution pruning reduces the beam codebook to a small subset of the most informative beams for beam allocation. To capture the distributional mismatch between the DT and real-world environments: the network was trained on imperfect digital-twin data but evaluated on real-world surrobase data. Results show that the proposed method achieves 96.80% top-1 and 98.36% top-3 beam prediction accuracy, while reducing over-the-air sounding overhead by nearly an order of magnitude relative to exhaustive sweeping, outperforming convolutional and deep neural network baselines from prior work.

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