We propose an efficient method to analyze the influence of thermal fluctuations of a molecular junction on its electronic transport properties, and consequently, reliably predict the time-averaged value of the conductance at a finite temperature in the zero-bias regime. Our multiscale approach combines three complementary techniques, namely, large-scale quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations, active machine learning methods, and nonequilibrium Green’s function transport calculations. Results for the exemplary Au(111)-S-C6H4-C6H4-S-Au(111) and Au(111)-N-C6H4-C6H4-N-Au(111) junctions indicate the substantial impact of the thermal evolution of the junction on its transport properties, which cannot be forecasted based just on the ground-state geometry. In the experimentally relevant temperature range around the room temperature, the predicted conductance values are 30–40% larger than those calculated for the minimum-energy configuration of the respective junction at 0 K.