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Pharmacy verification is a critical safety checkpoint that protects patients from inappropriate medication use before administration. Verification backlogs can emerge when complex orders, high patient demand, and limited pharmacist capacity converge. Backlog management is often reactive because supervisors typically recognize risk only after the queue is already growing. By that point, turnaround times may already be delayed and urgent orders may compete with routine workload. This article proposes a conceptual deep learning model to forecast pharmacy verification backlog depth over short operational horizons. The model integrates medication order characteristics, pharmacist staffing, patient acuity, high-alert medication flags, and historical queue dynamics. The proposed model would use timestamped medication orders, staffing indicators, acuity signals, and queue-state variables as a multivariate temporal input stream. A recurrent or temporal convolutional neural network could transform these inputs into predicted queue length and backlog probability for upcoming time windows. Conceptually, the model would provide early warning of impending verification congestion before the queue becomes operationally disruptive. Such forecasts could support pre-emptive pharmacist reallocation, prioritization of urgent orders, and improved shift-lead situational awareness. A deep learning approach could shift pharmacy operations management from retrospective queue monitoring toward proactive backlog prevention. This model-oriented framework offers a foundation for future prospective evaluation in high-volume hospital pharmacy environments.