Dynamic modulus (|E*|) measurements of hot-mix asphalt (HMA) mixtures are critical for understanding material behavior but present significant challenges due to the complex testing procedures and precision required. While substantial efforts have focused on developing predictive models for |E*|, the critical role of experimental data preprocessing has been largely overlooked in the existing literature. This study addresses this gap by proposing a novel, comprehensive framework for both preprocessing and post-processing of dynamic modulus data. Leveraging the well-established ASU |E*| database and the NCHRP 1–40D Witczak prediction model as benchmarks, we introduce advanced empirical techniques, including probability distribution analysis, scatter and box plots, correlation coefficients, and mutual information metrics, to refine data quality and enhance the interpretability of predictive models. Our approach reveals new insights into the interaction of various input factors and |E*| values, leading to improved model robustness and reliability. The post-processing techniques further substantiate the predictive power of the Witczak model, yielding significant enhancements in accuracy and reliability. This research pioneers a standardized data preparation methodology that sets a new precedent in asphalt material engineering, offering a robust foundation for future |E*| modeling and analysis.