Axial Spondyloarthritis (axSpA), as a chronic inflammatory disease, requires early diagnosis and treatment to improve patient outcomes. Traditional methods have many limitations in addressing the complexity and heterogeneity of axSpA, with particularly prominent issues of high misdiagnosis rates and long diagnostic delays. The introduction of machine learning technology has brought revolutionary breakthroughs in the diagnosis and treatment of axSpA. This review explores research advances and application potential of machine learning in the diagnosis and treatment of axSpA, evaluates the strengths and weaknesses of current models, analyzes the limitations of existing research, and proposes future development directions.