In order to check the applicability of Artificial Intelligent (AI) techniques to act as reliable inverse models to solve the multi-input/multi-output heat flux estimation classes of inverse heat transfer problems (IHTPs), in a newly reconstructed experimental setup, a two-input/two-two output (TITO) heat flux estimation problem was defined in which the radiation acts as the main mode of thermal energy. A simple three-layer perceptron Artificial Neural Network (ANN) was designed, trained, and employed to estimate the input powers (represent emitted heats-heat fluxes from two halogen lamps) to irradiative batch drying process. To this end, different input power functions (signals) were input to the furnace/dryer's halogen lamps, and the resultant temperature histories were measured and recorded for two different points of the dryer/furnace. After determining the required parameters, the recorded data were prepared and arranged to be used for inverse modelling purposes. Next, an ANN was designed and trained to play the role of the inverse heat transfer model. The results showed that ANNs are applicable to solve heat flux estimation classes of IHTPs.