## 1 Introduction  
There are different diseases affecting public health and safety across different parts of the world. Much research has centered on proferring solutions by diagnosing and truncating the transmission of diseases such as COVID-19 (henceforth, SARS-CoV-2) [1, 2], HIV [34], Ebola [5, 6, 7, 8], malaria [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], hereditary diseases [21, 22], monkeypox [23, 24, 25], tuberculosis [26, 27, 28, 29, 30], and other diseases [31, 32, 33, 34, 35], by adopting different computational, modeling and bioinformatics approaches. Recently, the World Health Organization (WHO) declared monkeypox disease a case of a global public health emergency of International Concern [36]. Health medications such as vaccines, and liquid and solid medications are very essential and in high demand to combat the diseases [37, 38, 39, 40]. After the production of these medications, one of the major problems is how to transport and distribute these health medications effectively and efficiently to regions where they are urgently needed. Health medications form an essential part of human society. However, the supply chain process that health medication undergoes before they are safely delivered to their various destinations can be very complicated and challenging. Issues such as unpredictability, instability, and a lack of accountability are problems facing the global supply chain sector. Companies today, (pharmaceutical firms inclusive), face a distinct set of difficulties, including transportation issues, outsourcing, bottlenecks due to unexpectedly high demand, in addition to growing consumer expectations, and complicated operational processes. Health medication supply chain fragility was recently brought to light by the outbreak of the SARS-CoV-2 pandemic [41; 42]. At present, organizations are seeking ways to make their supply chains less susceptible to disturbances in the current environment of transforming the supply chain competitive landscape, evolving workplace practices, and increasingly volatile demand [43]. Many well-known and new supply chain difficulties can be solved by computational approaches such as machine learning. Artificial intelligence helps companies manage their supply chains better and make them more resilient to disturbances. One of the ways supply chain managers can take to address pressing challenges in the supply chain process is to use machine learning use cases as a prepared roadmap of the steps they should follow [44; 45; 46]. Recently, the conventional linear supply chain has been evolving over the past few years into a computerized supply chain network, which we have all seen (DSNs). Businesses are actively reviewing their overall supply chain strategy in anticipation of the recent reality, and COVID-19 has only expedited this process [47]. It is feasible to convert conventional, linear supply chains into linked, adaptive, expandable, and configurable digitized supply chain networks with the aid of technologies like IoT, intelligent systems, and deep learning. Conventional supply chains operate according to a predetermined workflow. This is how most industrial execution systems work [48]. A nondeterministic system, on the other hand, lacks predetermined workflows and gives automation itself some latitude in how it manages business logic. Improved online manufacturing systems can reorganize and improve production processes, prevent service disruptions, and lower the expenses associated with changing product lines [49]. The widespread use of machine learning throughout the supply chain has made it possible for retailers, suppliers, and wholesalers to implement the massive adjustments that are urgently required to address the epidemic. Benefits that have never been feasible before are now available to supply chain operations thanks to machine learning, including cost savings via improved supply chain prediction and risk management, rapid supplies, and improved customer experience, to mention a few [50].