Background/Objectives: Accurate mediastinal and hilar lymph node (LN) assessment is central to lung cancer staging and treatment selection. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has become the standard minimally invasive procedure for mediastinal staging, enabling real-time identification, characterization and sampling of LNs. Although effective, this technique remains operator dependent, and subject to interobserver variability. This study aims to report the development of a Vision Transformer-based AI model for automatic LN detection and classification on EBUS images. Methods: A multicenter, multidevice, retrospective study, using EBUS images of three referral centers (Spain and Brazil). The dataset included images of patients with benign and malignant LNs. Bounding-box annotations were applied for LN localization, and LNs were classified as benign or malignant according to histopathologic reference standards. Detection performance was evaluated with mean average precision, at an intersection over union threshold of 0.5 (mAP50). Characterization performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results: The dataset included 6492 images from 27 patients, of whom 16 had malignant LNs. In the test set, the model achieved a mAP50 of 0.79 for LN detection. For malignant characterization, the model achieved an accuracy of 90.6%, sensitivity of 85.4%, and an AUROC of 0.938. Conclusions: The results of this study support the feasibility of AI-assisted EBUS interpretation for nodal staging workflows and raise the possibility that AI-assisted EBUS could enhance diagnostic performance, while reducing operator variability. Larger prospective studies are needed before clinical implementation.