In healthcare and retail, sectors where disruptions can have a high economic, operational, and human cost, supply chain resilience is very critical. The study will, therefore, take a comparative approach to such predictive models and advanced technologies as seek to improve the resilience of supply chains within these sectors. Using a large dataset encompassing various disruption factors, the study uses machine learning algorithms—Random Forests, Support Vector Machines (SVM), and XGBoost Regressors—to identify critical predictors of supply chain disruptions specific to the health and retail industries. Application of feature engineering and stringent evaluation metrics brings out key differences in how each sector benefits from predictive analytics and related technology advancements. In healthcare, supply chain disruptions can compromise patient care, especially when they delay critical medical supplies. Variables like "Late_delivery_risk" and "Days for Shipment (Scheduled)" become very important here, as even the slightest delay can have very serious consequences. Retail, on the other hand, represents a different set of vulnerabilities, dominantly influenced by changes in consumer demand and seasonal trends, thus requiring predictive models that can quickly adjust to dynamic market conditions. The present research, through the assessment of performance and impact, unravels that a tailored approach to predictive modelling is material for resilience maximization in every sector, thereby highlighting the importance of strong and sector-specific supply chains for industry-specific requirements.