Ultrasound (US) imaging is valued for its safety, affordability, and accessibility, but its low spatial resolution and operator dependence limit its diagnostic capabilities. Tomographic imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) offer high-resolution 3D visualization but are cost-prohibitive and complex. Ultrasound-based tomographic imaging aims to combine the advantages of both modalities, potentially democratizing access to advanced imaging. A scoping review was conducted following PRISMA-SR guidelines. Articles were identified through searches in PubMed MEDLINE, Embase, Scopus, and arXiv from inception to July 2025. Eligibility criteria included full-text original studies focused on ultrasound-based tomographic imaging generation or reconstruction methods. Out of 8256 identified articles, 86 met the inclusion criteria. Studies examined four imaging modalities: photoacoustic tomography (36%), ultrasound computed tomography (36%), 3D reconstruction (20%), and synthetic imaging (7%). Deep learning algorithms (67%) were the most common, followed by iterative reconstruction algorithms (9%), and other methods. The breast (17%), brain (16%), and blood vessels (14%) were the most studied anatomical regions. This review highlights advancements in ultrasound-based tomographic imaging, driven by deep learning innovations. Despite progress, the field is still in its infancy, and challenges remain in clinical adoption, particularly in standardization and validating performance. Future research should focus on improving algorithm efficiency, generalizability, and validation.