Chest X-ray image-based artificial intelligence (AI) systems have been highly active research in real-world medical applications recently, exactly due to their efficiency and effectiveness in supporting automatic computer-assisted diagnostics that would significantly alleviate radiologists’ workload, enhance the quality of patient care, and reduce the costs associated with surgical procedures. Motivated by this domain, we propose a novel comprehensive distillation framework, Multi-TeSt, which employs a knowledge distillation approach based on multiple teacher-student neural networks to aid physicians in comprehensively detecting and localizing misplaced tubes on chest X-rays. Since the well-known CNN met the drawback of data quality and processing speed while detecting misplaced tubes and catheters, our proposed method can handle and provide the following characteristics: (1) propose novel multiple teacher-student models for knowledge distillation; (2) improve the loss function efficiency during the training process. Our proposed method consists of two main stages. Specifically, in the first stage, we train the initial teacher model using annotated images, allowing the student model to learn features through the knowledge distillation mechanism. In the second stage, the learned student model is transformed into a teacher model to facilitate the training of another student model. This iterative approach enables the models to mutually enhance each other’s learning, leading to more accurate task completion. Experimental results demonstrate that our proposed method significantly outperforms traditional neural networks. Specifically, on the RANZCR dataset, our Multi-TeSt model achieves an Area Under the Curve (AUC) score of 0.9502 and an F1-score of 0.95. These results show a significant improvement over baseline models, including ResNet-200D and SE-ResNet-152D, in detecting and localizing catheter and line placement errors on X-ray images.