Accurately predicting the operational status of mining drilling systems under complex coal and rock conditions remains challenging due to the intricate dynamic characteristics of drilling tools, highly variable operating conditions, and the pronounced long-term dependence associated with drill-jamming risk. To address these challenges, this study proposes a deep temporal prediction network for forecasting the operational status of mining drilling systems. Specifically, a multiscale dilated causal convolutional network is employed to extract temporal features at multiple scales, thereby capturing dependencies over different time horizons. Subsequently, a temporal convolutional network (TCN) integrated with an attention mechanism is used to model multidimensional temporal correlations among the extracted features and adaptively assign channel-wise weights, thereby emphasizing informative features while suppressing irrelevant information. This architecture enables the accurate prediction of the drill-jamming factor. Furthermore, the joint objective supplements pointwise prediction error with a global temporal-correlation preference. The experimental results demonstrate that the proposed method achieves satisfactory predictive performance and captures temporal variation within the evaluated drilling sequence.