Modeling the methane emission is challenging due to the heterogeneity of solid waste characteristics and different chemical and physical reactions leading to methane generation. This study focused on monitoring the methane generation from landfills and modeling methane emission using machine learning techniques. Hence, two pilot landfills were constructed with a total capacity of 9327 tons of municipal solid waste. The temperature, methane, and leachate generation from the pilot landfills were measured for 3 years. The effect of leachate recirculation system on methane emission from landfill was evaluated, and the results showed that the methane emission was 35% lower when leachate recirculation system was not utilized in the landfilling process. Three machine learning models, including artificial neural networks, adaptive neuro‐fuzzy inference system, and support vector machine, were used for the first time to predict methane generation. Results demonstrated that the support vector machine model was superior to both the adaptive neuro‐fuzzy inference system and artificial neural network models for predicting methane generation. The support vector machine model was able to capture 90% and 82% of the variation in methane emission from landfills with and without leachate recirculation, respectively. In general, machine learning models showed considerable potential for forecasting methane generation.