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A small fraction of hospital episodes accounts for a disproportionate share of inpatient spending. Early recognition of these episodes remains difficult when risk assessment depends mainly on static admission information. More adaptive prediction is needed to support clinical and financial planning during hospitalization. Existing cost prediction models often emphasize claims, diagnoses, or broad utilization histories while underusing the dynamic signals that emerge during the inpatient stay. Pharmacy utilization, procedure sequencing, length-of-stay progression, and intensive care transfers may reveal escalating resource intensity before the final cost is known. Failure to integrate these modalities limits early identification of high-cost episodes. This article proposes a multimodal deep learning framework for predicting whether a hospitalization could become a high-cost outlier. The model is designed to combine pharmacy utilization, procedure sequences, length-of-stay trends, intensive care transfer events, and administrative claims data. The intended use is dynamic risk estimation early and repeatedly during the episode. The conceptual model uses separate modality-specific encoders for static claims features, temporal procedure events, pharmacy utilization patterns, length-of-stay trajectories, and intensive care transfer indicators. These representations are fused into a shared episode-level embedding trained with a cost-sensitive objective. The framework is intended for evaluation in historical and silent prospective deployment settings without assuming immediate clinical intervention effects. Conceptually, the model would output an updated probability that an active hospitalization will exceed a high-cost threshold. This probability would change as new medication orders, procedures, length-of-stay milestones, and intensive care transfers occur. The output could support utilization review, case management, pharmacy stewardship, and financial counseling workflows. A multimodal deep learning model for high-cost hospital episode prediction could help health systems identify emerging cost outliers before discharge. By combining static claims information with dynamic inpatient trajectories, such a model could support earlier resource allocation and more coordinated care planning. Its value should be assessed through careful validation, calibration, workflow integration, and prospective impact evaluation.