Universally applicable empirical equations specific for high- and low-forage diets were developed to improve the prediction of enteric methane production (eCH₄) from beef cattle. A database built using treatment means from published beef studies conducted in numerous countries was divided into two datasets: high-forage diet [≥40% forage dry matter (DM), n = 123] and low-forage diet (≤20% forage DM, n = 34). Monte-Carlo techniques were used to overcome the limited numbers of observations in each dataset, and multiple regression analysis and cross validation were used to develop new eCH₄ prediction equations. Precision, accuracy, and analysis of errors were evaluated using concordance correlation (r_c) and root mean square prediction error (RMSPE). The best-fit equations for high and low forage content included the following variables: body weight (kg) and intakes (kg d^-1) of DM, fat, neutral detergent fiber (NDF), acid detergent fiber, crude protein to NDF ratio, and starch to NDF ratio. For high and low forages, best-fit equations had r_c ≥ 0.70 and RMSPE ≤ 40 g eCH₄ d^-1 and r_c ≥ 0.90 and RMSPE ≤ 15 g eCH₄ d^-1, respectively. Use of equations specific to dietary forage proportion reduced the uncertainty of estimating beef cattle eCH₄ emission compared with the Intergovernmental Panel on Climate Change Tier 2 methodology.