To estimate more accurately the beef marbling score (BMS) of live beef cattle, the Bayesian network model (BNM) could be used in parallel with other developed methods, such as ultrasound (US) image analysis with a neural network (NN), biological impedance analysis (BIA) and visual inspections of an experienced inspector. Additionally, most of these methods individually represents positive trends of estimating subjective BMS in Japan. This research reveals that the approach of using BNM to include body condition parameters, exhibit more accurate estimation of BMS with other methods. The measurement was conducted with 28 Japanese Black Beef cattle before one-month slaughter. The weight, chest, abdominal circumference, and longissimus muscle area have been taken into consideration of body measurement parameters for evaluating BMS. The estimation of BMS with BNM, combined with other approaches displayed the higher accuracy rate of almost 90%. Moreover, this research compared the findings with other individual method and combined methods. The estimation of BMS using US image analysis using NN represents 28% accuracy, then BIA provides only 40%, and combing both US and BIA method illustrates 50% of accurate BMS estimation. However, Body condition indices, US and BIA together outreaches all estimation methods and the BNM provided more accurate estimation of BMS with high confidence.