An adaptive predictive control methodology is applied for a fossil fuel boiler control. The control algorithm takes advantage of a neuro-fuzzy identifier system for prediction of the boiler response in a future time window. An optimizer algorithm based on evolutionary programming technique (EP) uses the identifier-predicted outputs and determines input sequence in a time window. The present optimized input is applied to the plant, and the prediction time window shifts for another phase of plant output and input estimation. The neuro-fuzzy identifier is trained to provide a good estimation of boiler outputs. Neuro-fuzzy rules and membership parameters are trained based on the data log, applying genetic algorithm and back-propagation, respectively. The obtained intelligent control system is highly structural and applicable on different boiler systems.