Rhabdomyolysis (RM) is a multifactorial clinical syndrome characterized by the disintegration and necrosis of muscle tissue, leading to the release of cellular contents into the circulation. One of the most severe complications of RM is acute kidney injury, with a mortality rate of 20%-50%. Early and timely diagnosis is the key to improving the prognosis of patients with RM. The etiology of RM is complex and associated with various traumas, drugs, medications, and hereditary diseases, and the clinical symptoms are nonspecific. Therefore, its diagnosis highly relies on the doctor's experience and the level of medical equipment. However, RM often occurs in situations with limited medical resources, such as natural disasters, battlefields, and large-scale traffic accidents. In these scenarios, the varying levels of expertise among rescue personnel can lead to delays in diagnosis and treatment, thereby increasing the risk of mortality. This article provides a comprehensive review of the etiology, pathogenesis, complications, diagnostic, and treatment methods of RM. It also aims to offer new perspectives on the diagnosis and prognosis of RM by integrating machine learning and artificial intelligence. It is believed that this article can help pre-hospital rescuers and in-hospital doctors have a comprehensive understanding of RM to improve the patients' outcomes and overcome the challenges.