The 2022 Mpox outbreak emphasized the need for effective forecasting tools to support public health responses. This study evaluates four epidemiological models, SEIR, Richards, Gompertz, and GLM, for short-term prediction of the 2022 Mpox outbreak in the United States, using CDC data from 12 May to 15 December 2022. We compare model perfor- mance across 1- to 4-week horizons with the Normal, Poisson, and Negative Binomial error structures, using metrics such as MAE, MSE, WIS, and 95% PI Coverage. The Richards model with a Normal error structure excelled, achieving the lowest MAE (227.81–283.88) and WIS (163.67–214.24), improving 29.2–36.3% in MAE over SEIR. The GLM showed competitive accuracy, while Gompertz led in uncertainty quantification (95% PI Coverage: 39.26–40.75%). The Normal error structure consistently outperformed others. These findings highlight the Richards model’s potential for early warning and inform targeted interventions, enhancing public health strategies for Mpox and similar diseases.