Catheters are life support devices implanted in the body for draining fluid or air from an opening in the body (a cavity or a duct). When inserted in the body, catheters administer the smooth flow of medications, gases, or fluids into the body. The process of ensuring proper placement of a catheter in the body involves taking X-ray images to evaluate the catheter's location soon after insertion, as malpositioning may cause several problems. Manual detection and classification require error-free and quick human intervention, which is often not fully possible. Leveraging the immersive capabilities of the metaverse, combined with deep neural network-based approaches, can enhance the detection and classification of catheters in chest X-rays. However, existing techniques are able to analyze only a few numbers of catheters. In this chapter, we perform augmentations and parameter adjustments to address the problem of detection and classification of four different classes of catheters: endotracheal tube (ETTs), nasogastric tube (NGT), central venous catheters (CVCs), and Swan-Ganz into 11 different positions. Using the transfer learning paradigm, the EfficientNet B7 detects abnormal positioning and predicts the proper placement of catheters in real-time within the metaverse environment, with an AUC score of 0.945. Thus the approach can be very useful to aid medical practitioners in the proper placement of catheters, including performing any adjustments in position required, all within a metaverse-based simulation that provides a more intuitive and interactive experience. © 2025 Elsevier Inc. All rights reserved.