## 1 Introduction
Nevertheless, a lot of businesses are increasingly utilizing both types of storage, particularly when a data lake serves as the foundation for a data warehouse that leverages sanitized and structured data from a DL [63]. Another type of supply chain ML application is computer vision (CV) for inventory control. It is often used in many different settings. It is first used to categorize and tally freshly delivered items. CV also helps in the identification of visible package damage. With the use of computer vision, the program can classify the items it \"sees.\" Robots equipped with cameras, for example, may scan your storage spaces and instantly produce a picture of your goods. Machine learning techniques that may be applied in the CV sector include supervised learning, unsupervised learning, and reinforcement learning [64]. Predictive maintenance of equipment is another common application of machine learning in the supply chain. Based on real-time asset data instead of a predetermined timetable, ML ensures reactive and preventative maintenance of equipment. Supply chain experts can drastically reduce maintenance costs by improving asset upkeep. Additionally, ML aids in the decline of no-fault-found (NFF) situations. When a unit is deemed to be defective, it is taken out of service and designated as NFF. If no abnormality is found, the device is put back into use without any repairs. The production process becomes more efficient as the number of such accidents decreases [65]. ML aids in determining a package's location throughout the logistics process. It enables supply chain experts to monitor the whereabouts of cargo while it is being transported. Additionally, it gives insight into the circumstances of the package's transportation. Retailers can keep an eye on variables like temperature, vibration, humidity, etc. with the use of sensors. Additionally, ML supports in-the-moment route optimization. It keeps track of the weather and the state of the roads and makes suggestions on how to shorten travel time and optimize the route. This allows for the diversion of trucks at any moment when a more economical route is available [66]. Machine learning is utilized in warehouses to automate manual tasks, foresee potential problems, and minimize paperwork for warehouse workers. For instance, computer vision enables the management of conveyor belt operations and the forecasting of blockages. Thanks to NLP and OCR [67], warehouse personnel can automatically recognize goods' arrival and modify their delivery statuses. On the product, barcodes and inscriptions are scanned by cameras, and the data are immediately input into the system. Additionally, machine learning assists in programming robots and autonomous vehicles, both of which are commonly utilized in warehouses. Autonomous vehicles and robots assist with receiving, packing and unpacking, transporting, and uploading and unloading boxes with the use of system-integrated instructions. In this scenario, computer vision aids in locating a vacant space for a box, monitoring its proper placement, and preventing robot and automobile accidents in warehouses [68]. This paper is an innovative work because it deals with the hybridization of deep learning and Bayesian optimization with a tree parzen estimator for the classification of the supply chain of health medication datasets. To the best of our knowledge, there is no study in the literature that has adopted such a technique. The major contributions of this work include:  
1. A survey of machine learning and deep learning algorithms that have been applied for supply chain management was presented. 2. Determination of the most appropriate deep learning model for the classification of supply chain health medications was done. 3. Development of deep learning and Bayesian optimization Techniques with Tree Parzen Estimator for classification of supply chain health medications was done. 4. Evaluation of the performance of the proposed methods for classifying health medications using different metrics was achieved.