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Clinical service line margins are increasingly pressured by value-based reimbursement, payer-specific contracting, and operational constraints that alter the relationship between clinical activity and financial performance. Traditional forecasting remains largely anchored in static budgets and delayed variance reports rather than continuously updated clinical and operational signals. Current financial planning tools often cannot dynamically incorporate evolving case mix, payer contracts, length of stay, resource consumption, and throughput patterns before deviations appear in the general ledger. As a result, service line leaders may recognize margin deterioration only after financial corrective action is already delayed. This article proposes a deep learning model for predicting clinical service line financial performance, including revenue, cost, and contribution margin. The model is designed to integrate clinical, operational, resource utilization, payer, and volume-based predictors into a unified forecasting framework. The proposed approach uses a temporal deep learning architecture that fuses static service line characteristics with dynamic monthly features. It would generate forward-looking financial forecasts with uncertainty-aware outputs and interpretable drivers for service line executives. Conceptually, the model would identify a pending margin shortfall driven by a combination of rising patient acuity, unfavorable payer contract exposure, higher resource consumption, longer length of stay, and declining procedure volume. Such forecasts would support earlier operational review and targeted cost-management actions before formal budget variance escalation. A deep learning model for service line financial forecasting could enable continuous surveillance of revenue, expense, and margin risk. By linking clinical activity, payer dynamics, and operational throughput, the approach could support proactive decision-making by service line directors and health system finance leaders.