This study develops and validates a predictive model for outcomes in cervical spondylosis (CS) patients after anterior cervical discectomy and fusion (ACDF). To conduct this, data on 973 patients (872 retrospective & 101 prospective) were recorded, and LASSO regression was used to identify 10 essential predictive features to be fed into 10 machine learning algorithms (DT, RF, XGBoost, LASSO regression, SVM, MLP, LightGBM, KNN, logistic regression, and stacking ensemble learning). SPSS (IBM version 26.0) and R (version 4.2.2) software were used to determine the statistical significance of the models' predictions against the results of a 1-year follow-up. These models were assessed using area under the curve (AUC) values, decision curve analysis (DCA), and calibration curve evaluations. With the stacking ensemble learning model achieving an internal validation set, external validation set, and prospective cohort AUC of 0.81, 0.80, and 0.82, respectively, it was determined to be the best predictor by far. In this experiment, it is important to note that data collection occurred over 3 spine centers, potentially resulting in error due to differing clinical practices and methods. Future studies should employ cross-institutional collaboration.