Methane (CH 4 ) emissions produced by dairy cattle (DC) are a key contributor to global warming. To assess the effectiveness of strategies designed to mitigate CH 4 emissions, complex and expensive recording equipment is required. Therefore, the use of predictive models based on animal information provides a more accessible alternative. Traditionally, Statistical (SA) methods have been employed in the prediction of DC CH 4 emissions. However due to the smart farming revolution, the scale and variety of complex animal information now available for the prediction of DC CH 4 emissions has grown exponentially, and within them are likely to exist non-linear relationships which these traditional SA models may struggle to capture. Therefore, this research aims to explore if Machine Learning (ML) models are a viable alternative for the prediction of DC CH 4 emissions, as they can handle and extract these inevitable non-linear relationships present within today's large, heterogeneous datasets. In this research, we compared a traditional SA method, a Linear Mixed Effects (ME) model, with an original ML method, a Random Forest (RF) model, as well as a novel SA/ML hybrid method, a Mixed Effects Random Forest (MERF) model, in the prediction of CH 4 emissions (CH 4 g/d) produced by DC across 32 experiments. The ML RF model was able to challenge the traditional SA ME model in the prediction of DC CH 4 emissions, achieving a Root Mean Square Prediction Error (RMSPE) and Concordance Correlation Coefficient (CCC) of 52.73 CH 4 g/d and 0.70 respectively, compared to a ME model’s 53.90 CH 4 g/d and 0.71. When both the ME and RF models were combined within the novel SA/ML hybrid MERF model, a lower RMSPE and higher CCC were achieved than by each of its composite parts in isolation, 51.87 CH 4 g/d and 0.73 respectively. These results demonstrate the potential of ML in the prediction of DC CH 4 emissions, particularly when hybridised alongside traditional SA methods.