This study presents an enhanced version of the Exhaustive Objective Ranking Solution (EORS) method for multi-criteria decision-making (MCDM), integrating Kernel Density Estimation (KDE) to improve precision in solution evaluation. Unlike the original histogram-based approach, the KDE-based variant offers a continuous and smoother representation of preference distributions. A comparative analysis using a theoretical case study demonstrates that the improved method yields more accurate and interpretable results while maintaining strong agreement with the original. Key metrics such as Degrees of Confidence and similarity coefficients confirm the enhanced robustness of the KDE-based approach. The results underscore the method’s potential to better support objective decision-making, especially under uncertainty.