The aim of the research described in the present work is exactly the modeling of the temperature dependencies of E′(T), E′′(T), and tan δ(T) of the investigated TPU submitted to DMA tests at a constant single frequency of dynamic mechanical loading in the whole temperature interval of its service life, or from its glassy state up to the flow region, using the multi-layer feed-forward ANNs utilizing the back-propagation learning algorithm.