Background: Arachnoid cysts are fluid collections within the arachnoid membrane. Although rare, ruptured or hemorrhagic arachnoid cysts pose significant clinical challenges and management controversies. The present study analyzes factors influencing their treatment decision using Bayesian network models. Methods: PubMed and EMBASE databases were searched to identify reports of ruptured arachnoid cysts with patient-level data. Demographic, clinical, imaging and treatment data were extracted to develop Tree-augmented naïve Bayes (TAN) classifiers for analyzing the factors influencing decision of surgery and type of surgery. A web application was developed to explore the networks. Results: Middle cranial fossa cysts were most common (95%) along with a male predominance (M:F ratio 4.29:1). Headache and vision changes were the most common symptoms and >50% had a history of head injury. Surgery was performed in 89.8% of cases with craniotomy being the most common surgical procedure. Key factors influencing the decision of surgery were cyst location, hemorrhage type, age group, and Galassi classification, while type of surgery was also influenced by head trauma, seizures, and macrocrania. Conclusions: Bayesian network analysis demonstrates that decision of surgical treatment of a ruptured arachnoid cyst is dependent on multiple interdependent factors and should be individualized to match the presentation with the surgical modality.