Quantitatively, entropy regularization reduces DRT recovery errors by approximately 50% compared to RR while maintaining high impedance fitting accuracy. Moreover, this study advances DRT analysis through two key methodological innovations. The first is a robust framework for uncertainty quantification, achieved by applying Monte Carlo sampling to the entropic regression DRT method within a Bayesian framework. The second is a versatile multi-spectrum processing capability that enables systematic tracking of electrochemical dynamics across varying experimental conditions. We validate these advances through extensive testing on diverse electrochemical systems, including lithium-ion batteries, fuel cells, and solar cells, and demonstrate that entropy-based deconvolution significantly enhances both the reliability and interpretability of DRT analysis in electrochemistry and related fields.