Ultrasound (US) imaging is one of the most accessible, non-invasive, and real-time diagnostic techniques in clinical medicine. However, conventional B-mode US suffers from intrinsic limitations such as speckle noise, operator dependence, and variability in image interpretation, which reduce diagnostic reproducibility and hinder skill acquisition. Because accurate image acquisition and interpretation rely heavily on the operator’s experience, mastering ultrasound requires extensive hands-on training under diverse anatomical and pathological conditions. Yet, traditional educational settings rarely provide consistent exposure to such variability, making simulation-based environments essential for developing and standardizing operator expertise. This scoping review synthesizes advances from 2014 to 2024 in B-mode ultrasound simulation, identifying 80 studies through structured searches in PubMed, Scopus, Web of Science, and IEEE. Simulation methods were organized into interpolative, wave-based, ray-based, and convolution-based models, as well as emerging Artificial Intelligence (AI)-driven approaches. The review emphasizes recent simulation engines and toolboxes reported in this period and highlights the growing role of learning-based pipelines (e.g., Generative Adversarial Networks (GANs) and diffusion) for realism, scalability, and data augmentation. The results show steady progress toward high realism and computational efficiency, including Graphics Processing Unit (GPU)-accelerated transport models, physics-informed convolution, and AI-enhanced translation and synthesis. Remaining challenges include the modeling of nonlinear and dynamic effects at scale, standardizing evaluation across tasks, and integrating physics with learning to balance fidelity and speed. These findings outline current capabilities and future directions for training, validation, and diagnostic support in ultrasound imaging.