This article demonstrates the external validation of the Skeletal Oncology Research Group machine learning algorithm (SORG-MLA), originally developed in the United States, on a Taiwanese cohort to predict prolonged opioid use after anterior cervical discectomy and fusion (ACDF). The original SORG algorithm was trained on 2,737 U.S. patients, split 80:20 into training and testing sets, who had undergone ACDF between 2000 - 2018. Various machine learning models were tested (stochastic gradient boosting was superior in discrimination, calibration, and performance) and incorporated 9 clinical and demographic variables to estimate individualized risk - sex, multilevel procedure, insurance type, tobacco use, use of Beta-2 agonists, gabapentin use, ACE inhibitor use, antidepressant use, and duration of preoperative opioid use. In the Taiwanese validation study, 1,037 Taiwanese patients (from 2010 - 2018) were assessed, with SORG-MLA’s performance evaluated through discrimination (AUROC = 0.78), balance of precision and recall (AUPRC = 0.35), and clinical utility analyses. The algorithm demonstrated strong performance, with an 18% increase in prediction accuracy compared to the null model (Brier score = 0.033), though it did overestimate prolonged opioid use (predicted around 8% vs. observed 3.3%). Overestimation was attributed in the study to cultural tendencies in Taiwan to withhold pain reports, tighter opioid control policies, and demographic differences from the U.S. cohort.