Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using machine learning in the field of eating disorders is just beginning to emerge. This paper provides a narrative review of existing research and explores potential benefits, limitations, and ethical considerations of using machine learning to aid in the detection, prevention, and treatment of eating disorders. Current research primarily uses machine learning to predict eating disorder status from females' responses to validated surveys, social media posts, or neuroimaging data often with relatively high levels of accuracy. This early work provides evidence for the potential of machine learning to improve current eating disorder screening methods. However, the ability of these algorithms to generalise to other samples or be used on a mass scale is only beginning to be explored. One key benefit of machine learning over traditional statistical methods is the ability of machine learning to simultaneously examine large numbers (100s to 1000s) of multimodal predictors and their complex non-linear interactions, but few studies have explored this potential in the field of eating disorders. Machine learning is also being used to develop chatbots to provide psychoeducation and coping skills training around body image and eating disorders, with implications for early intervention. The use of machine learning to personalise treatment options, provide ecological momentary interventions, and aid the work of clinicians is also discussed. Machine learning provides vast potential for the accurate, rapid, and cost-effective detection, prevention, and treatment of eating disorders. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are important limitations and ethical considerations with utilising machine learning methods in practice. Thus, rather than a magical solution, machine learning should be seen as an important tool to aid the work of researchers, and eventually clinicians, in the early identification, prevention, and treatment of eating disorders.Machine learning models are computer algorithms that learn from data to reach an optimal solution for a problem. These algorithms provide exciting potential for the accurate, accessible, and cost-effective early identification, prevention, and treatment of eating disorders, but this potential is just beginning to be explored. Research to date has mainly used machine learning to predict women’s eating disorder status with relatively high levels of accuracy from responses to validated surveys, social media posts, or neuroimaging data. These studies show potential for the use of machine learning in the field, but we are far from using these methods in practice. Useful avenues for future research include the use of machine learning to personalise prevention and treatment options, provide ecological momentary interventions via smartphones, and to aid clinicians with their treatment fidelity and effectiveness. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are limitations and ethical considerations with using these methods in practice. If accurate and generalisable machine learning models can be created in the field of eating disorders, it could improve the way we identify, prevent, and treat these debilitating disorders.