Reliable roof-water-inrush risk zoning in coal mines remains difficult under small-sample conditions because geological, hydrogeological, and mining-induced factors interact nonlinearly, and field labels are often derived from engineering evidence rather than complete accident records. This study aims to develop and preliminarily validate a deployment-oriented workflow for case-specific roof-water-inrush risk-zone classification in the No. 4−3 coal seam of Shiyangou Coal Mine, Shaanxi Province, China. A total of 100 spatial evaluation samples were compiled using eight indicators grouped into water-resisting capacity, water-supply intensity, water-conducting pathways, mining-induced disturbance, and rock-mass integrity. The Safe-Zone and Risk-Zone labels were generated from mine-water-prevention documents, borehole and hydrogeological evidence, abnormal goaf-water information, and expert engineering judgment. A lightweight CNN-LSTM model was trained with leakage-controlled data splitting, early stopping, L2 regularization, gradient clipping, and model-capacity control. Because the inputs are static or quasi-static spatial indicators, LSTM was used only as a gated feature-dependency module for CNN-derived feature sequences, and its necessity was examined using ablation and feature-order perturbation tests. In repeated stratified five-fold cross-validation, the model achieved mean accuracy, F1-score, and AUC values of 0.792 ± 0.108, 0.633 ± 0.111, and 0.829 ± 0.097, respectively. On an independent prediction dataset, the accuracy and AUC were 0.750 and 0.743, respectively. After ONNX export, STM32CubeMX evaluation indicated 68,962 B Flash and 3252 B RAM consumption. These results indicate the preliminary feasibility of embedded roof-water-inrush risk-zone classification for this specific mine case. However, the findings do not demonstrate cross-mine transferability; further validation using external datasets, independent labels, and field deployment records is required.