Drug shortages and supply-chain interruptions pose significant risks to patient care, disrupt clinical decision-making, and generate substantial operational costs for health systems, often preceded by observable signals in sourcing, manufacturing capacity, regulatory quality events, and demand volatility. Current pharmaceutical supply-chain risk management largely relies on manual monitoring, retrospective shortage reporting, and simple threshold-based escalation, which are limited in their ability to integrate heterogeneous signals or detect emerging disruption patterns that deviate from historical events. To address these limitations, this article proposes a conceptual predictive model designed to estimate the probability and timing of pharmaceutical supply-chain disruptions, with a focus on sourcing risks, manufacturing capacity constraints, regulatory inspection signals, and demand-side volatility. The approach leverages a gradient-boosted classification model trained on historical shortage events and aligned time-series predictors, including features such as supplier concentration, plant capacity utilization, enforcement action indicators, drug-specific demand trends, and seasonality. Conceptually, the model would function as an early warning system for high-risk supply nodes, ranking contributing factors to enable targeted mitigation before shortages impact patients. By integrating operational, regulatory, sourcing, and demand signals, this predictive framework has the potential to shift pharmaceutical supply-chain management from reactive response to proactive resilience planning, enhancing medicine availability and strengthening public health preparedness.