Background: Necrotizing fasciitis (NF) is a life-threatening infection of the skin and soft tissue that spreads quickly and requires immediate surgery and medical treatment. Amputation or radical debridement of necrotic tissue is generally always required. The risks and benefits of both the surgical options are weighed before deciding whether to amputate or debride. This study set forth to create an easy-to-use risk scoring system for predicting the risk scoring system for amputation in patients with NF (ANF). Methods: This retrospective study included 1,506 patients diagnosed with surgically confirmed NF at three general hospitals in Thailand from January 2009 to December 2012. All diagnoses were made by surgeons who strictly observed the guidelines for skin and soft tissue infections produced by the Infectious Diseases Society of America. Patients were randomly allocated to either the derivation (n = 1,193) or validation (n = 313) cohort. Clinical risk factors assessed at the time of recruitment were used to create the risk score, which was then developed using logistic regression. The regression coefficients were converted into item scores, and the total score was calculated. Results: The following four clinical predictors were used to create the model: female gender, diabetes mellitus, wound appearance stage 3 (skin necrosis and gangrene), and creatinine ≥1.6 mg/dL. Using the area under the receiver operating characteristic curve (AuROC), the ANF system showed moderate power (78.68%) to predict amputation in patients with NF with excellent calibration (Hosmer-Lemeshow χ2 = 2.59; p = 0.8586). The positive likelihood ratio of amputation in low-risk (score ≤ 4) and high-risk (score ≥ 7) patients was 2.17 (95%CI: 1.66-2.82) and 6.18 (95%CI: 4.08-9.36), respectively. The ANF system showed good performance (AuROC 76.82%) when applied in the validation cohort. Conclusion: The developed ANF risk scoring system, which includes four easy to obtain predictors, provides physicians with prediction indices for amputation in patients with NF. This model will assist clinicians with surgical decision-making in this time-sensitive clinical setting.