Bioreactor landfills (BRLs) aim to increase moisture content of municipal solid waste to enhance the biodegradation kinetics of the organic fraction and biogas production. Prediction of biogas production is a key tool to design an appropriate energy recovery system from BRLs. In this paper, a fuzzy-based model to predict methane generation in full scale BRLs is proposed. Eleven deterministic inputs (pH, RedOx potential, chemical oxygen demand, volatile fatty acids, ammonium content, age of the waste, temperature, moisture content, organic fraction concentration, particle size and recirculation flow rate) were identified as antecedent variables. Two outputs, or consequents, were chosen: methane production rate and methane fraction in the biogas. Antecedents and consequents were transported in the fuzzy domain by a fuzzyfication procedure and then linked by 84 IF-THEN rules, which stated the effects of the input parameters in a linguistic form. The fuzzy model was built and tested on seven lab-scale studies, representing different operational conditions and waste qualities. The fuzzy model showed good performances in the prediction of methane generation, although lab-scale studies depicted ideal conditions that can be hardly reached in real BRLs. In order to deal with higher heterogeneities and lower data availability typical of full-scale landfills, new antecedents and rules were added to the proposed model. With few adjustments based on the available information, the fuzzy model could be applied to a retrofit BRLs located in Northern Italy. The results confirmed that fuzzy macro-approach can be a powerful and flexible tool able to model the complex processes taking place in BRLs.