Industrial Internet-of-Things (IIoT) environments continuously generatemassive, heterogeneous streams of sensor readings, operational logs, andalarm messages, demanding intelligent systems capable of real-time decisionsupport across forecasting, anomaly detection, and natural-languageexplanation. Existing time-series foundation models address these tasks inisolation and ignore the rich graph-structured topology inherent toindustrial sensor networks, while general-purpose large language models(LLMs) lack efficient mechanisms for processing high-frequency numericalstreams under strict latency constraints. We propose BRIDGE(Big data-driven Real-time Industrialmulti-modal Decision support via Graph-Enhancedmulti-modal transformer), a unified pre-trained framework integrating threecomplementary modules: (i) a Graph-enhanced Topology Encoder (GTE)that captures latent sensor-network dependencies via multi-head graphattention, enriching temporal representations with physical topologypriors; (ii) an Extended-LSTM Temporal Backbone (xLSTM-TB) thatleverages true recurrence for coherent long-horizon probabilisticforecasting, augmented by our proposed Contiguous Graph PatchMasking (CGPM)---a graph-aware pre-training strategy that jointly maskstopologically adjacent sensor patches to improve robustness under realisticsensor-outage conditions; and (iii) a Cross-modal LLMAlignment (CLA) module that reprograms heterogeneous industrial tokensinto a frozen LLM's representation space using fewer than \((0.3)\)M additionalparameters, enabling zero-shot natural-language decision reports withoutbackbone fine-tuning. Pre-trained on a curated corpus of \((78)\) millionheterogeneous industrial time-series samples spanning manufacturing, energy,transportation, and cloud-infrastructure domains, BRIDGE is evaluatedon the GiftEval-ZS and Chronos-ZS zero-shot forecasting benchmarks and fourindustrial anomaly-detection benchmarks (SMAP, MSL, SMD, SWaT).BRIDGE achieves state-of-the-art zero-shot CRPS of \((\mathbf{0.396})\)on GiftEval-ZS, surpassing TiRex by \((3.7%)\) and TimesFM 2.0 by \((13.7%)\),while simultaneously attaining an \((F_1)\) score of \((\mathbf{92.4%})\) on SMAPanomaly detection and producing interpretable decision reports withROUGE-L of \((0.74)\)---all within an end-to-end inference latency of\((\mathbf{12})\) ms, making BRIDGE the first framework to jointly achieveindustrial-grade forecasting accuracy, topology-aware anomaly detection,and explainable real-time decision support.