In order to improve prognostic precision, this study compares the accuracy of a novel Random Forest algorithm—which predicts distinct stages of goiter—with logistic regression. Twenty samples total from the study are divided into two groups: Group 1 uses Random Forest with ten iterations, while Group 2 uses ten iterations of Logistic Regression. The confidence interval (CI) is set at 95% and statistical power at 80%. When compared to the Logistic Regression approach (70.72%), the Random Forest algorithm exhibits higher accuracy (92.9%). With error rates of 92.9% and 70.72%, respectively, Random Forest Memory outperforms Logistic Regression in terms of accuracy.