Supply chains are encountering more uncertain conditions and risks. Disruptions that impede the flow of material through a supply chain that can also result in failure to deliver end goods are a significant category of risks. The consequence of the Covid-19 outbreak has led to shut down production in the supply chain system, resulting in significant impediments for many foreign supply-dependent enterprises. The constraints cause substantial disruptions of the supply chain, production delays, and supplier delays. In recent years, managing supply chain risks has been given more importance to protect supply chains from interruptions by forecasts and prevention. The effects of disruptions on logistics, costs, demand, profits, and inventory levels of the supply chain are analyzed. SVM is one of the most convenient and effective supervised learning algorithms commonly used for classification and regression challenges. This paper presents a modernistic machine learning model, multi-category support vector machines (MC-SVM)  algorithm through training on selected samples. In order to abet MC-SVM model to perform well on  imbalanced data, k-means clustering algorithm has been proposed to classify clusters of nodes at-disruption, which share similar interruption profiles and can find the relationships between the data object, provide massive information and contribute significantly to accelerating classification and prediction of  the SVM model. Data from portfolios of different firms in pharmaceutical industry has been used to train the MC-SVM model which maps the economic performance of a firm to a certain type of supply chain disruption (SCD). The potentiality of this research will privilege stronger control of the supply chain and thus will permit a network to offer better responsiveness to the customer, lower costs in all stages of the chain, lower inventories throughout the chain and diminish the bottleneck effect in the supply chain logistics.