In this paper, a reduced-order model built from the reduced basis (RB) method is used as a surrogate for an inverse identification problem related to coupled thermo-hydro-mechanical (THM) processes. The RB space—spanned by solutions of the governing THM equations—is constructed using a greedy adaptive procedure guided by an a posteriori error estimator that selects the optimal snapshot points in a given parametric space. The RB model is assessed in terms of accuracy and computational cost reduction for the three-dimensional transient coupled problem described by the ATLAS III small-scale in situ heating test. The substantial system size reduction and the associated significant computational gain result in a surrogate model suitable for parameter identification procedures in the Boom Clay material. The effectiveness of the proposed strategy is demonstrated by performing inverse analysis based on direct-search and genetic algorithm (GA) optimization supported by real sensor measurement data where 800 times faster computational speed-up was achieved.