This study advances DRT deconvolution by introducing entropy-based regularization as an alternative to conventional matrix differentiation penalties. The method improves DRT reconstruction accuracy while effectively suppressing spurious peaks that often compromise standard regularization techniques. Performance remains consistent across impedance models and experimental conditions, including scenarios with high noise levels, and it enables uncertainty quantification through DRT sampling. Moreover, the entropy-based method's extension to multiple experiments enables concurrent analysis of impedance spectra measured across different experimental conditions, providing insights into how electrochemical processes evolve with operational parameters. Analysis of experimental data from batteries, fuel cells, and solar cells demonstrates that the entropy-based approach yields interpretable relaxation time distributions while maintaining accurate impedance regressions. This capability in handling multidimensional datasets facilitates the investigation of electrochemical process dependencies on operational parameters.