Objectives: The femoral cartilage in the knee joint is prone to degenerative changes and injuries, often requir-ing Magnetic Resonance Imaging as the diagnostic gold standard. However, due to the high cost and limited availability of Magnetic Resonance Imaging, ultrasound is explored as a viable alternative. This paper presents a comprehensive review of current deep learning (DL) strategies for knee femoral cartilage segmentation in ultrasound images, focusing on commonly used datasets, data preprocessing techniques, and state-of-the-art DL models. Methods: We systematically reviewed the literature from major medical and engineering databases, sum-marizing key contributions to knee femoral cartilage segmentation. We focused on (1) the scarcity of large-scale public ultrasound datasets and its impact on model training, (2) popular DL architectures (e.g., U-Net variants, Mask Region-based Convolutional Neural Network), and (3) evaluation techniques, particularly the Dice Similarity Coefficient. We also examined image preprocessing and data augmentation strategies aimed at mitigating data insufficiency. Results: Our review shows that U-Net and its variants (e.g., Siam U-Net, U-Net++) commonly achieve competitive Dice Similarity Coefficient values (around 0.70–0.80) for knee cartilage segmentation, despite the limitations in training data. Advanced networks like Mask Region-based Convolutional Neural Network, when com-bined with robust image preprocessing and transfer learning (e.g., using COCO/ImageNet pretrained weights), can improve Dice Similarity Coefficient by over 20%. However, the absence of standardized public datasets limits direct comparisons between studies and affects reproducibility. Conclusion: DL holds significant potential for accurate and cost-effective femoral cartilage segmentation in knee joint ultrasound images, offering a feasible alternative or complement to Magnetic Resonance Imaging-based assessments. However, challenges remain due to the lack of large-scale, open-access ultrasound datasets and inconsistent evaluation protocols. Future work should focus on establishing public benchmarks, refining novel network architectures, and enhancing real-time clinical deploy-ment to foster wider adoption and greater clinical impact.