Pulmonary Hypertension (PH) is associated with cardiopulmonary disease and carries strong prognostic information. Its accurate diagnosis is based on invasive procedures such as right-sided heart catheteriza-tion. Alternative non-invasive approaches rely on tricuspid Doppler imaging, where signal quality may impede correct readings potentially limiting its clinical value. In this study, we propose an automated approach for the quality assessment of Doppler signals using a convolutional neural network (CNN). The CNN was trained on Doppler images and their quality was assessed by expert readers. The dataset was subjected to preprocessing and augmentation techniques to enhance model resilience and generalization. Leveraging the VGG-16 architecture, the CNN demonstrated an accuracy of 82%, sensitivity of 82%, precision of 97%, and F1-Score of 86% on the test set. The CNN showed improved precision, recall, and F1-score as compared to a secondary clinical reader assessment. Although this approach shows potential, further improvements, such as enlarging the dataset, are essential for clinical applicability. Deep learning driven image quality assessment could enhance diagnostic accuracy, reduce practitioner variability, and streamline patient care in PH management.