In recent years, multimodal ultrasound has become an important tool for the diagnosis and treatment of arthritis due to its advantages of real-time dynamic imaging, non-radiation, and multi-joint synchronous examination. The integration of artificial intelligence and musculoskeletal ultrasound effectively solves the limitations of traditional ultrasound in terms of operational variability, image interpretation subjectivity, and data analysis time, significantly improving diagnostic efficiency and accuracy. Bacterial arthritis and non-bacterial arthritis, as two major categories of arthritis, are different in incidence rate, treatment, and prognosis. This review aims to explore the research progress and clinical potential of multimodal ultrasound and artificial intelligence in distinguishing between bacterial arthritis and non-bacterial arthritis, in order to provide a reference for improving diagnostic accuracy and guiding treatment decisions.