The present work proposes a fast and optimized experimental approach for pressure reconstruction and far-field noise prediction for flow past tandem cylinders based on time-resolved particle image velocimetry (PIV). The low-order reconstruction of the velocity fields based on proper orthogonal decomposition (POD) is applied, which effectively mitigates the incoherent measurement noise by selecting the low-order modes representing the dominant coherent structures. The preprocessing of velocity fields significantly improves the accuracy of both field and surface pressure fluctuations estimated by solving the Poisson equation. The time-marching enhancement algorithm uses the pressure field from the preceding snapshot as the initial guess in the iterative process, which accelerates convergence and reduces the computational cost for solving the Poisson equation of the PIV database with a large ensemble size. The estimated surface pressure fluctuations are used to predict the far-field noise through Curle’s analogy with the correction based on the spanwise correlation length. Comparisons are performed with reference signals, yielding good agreement on both pressure and noise spectra.