This paper focuses on developing a Quantitative Structure-Property Relationship (QSPR) model using distance-based topological indices for anti-tuberculosis (TB) medications and their physicochemical features. The study employs chemical graph theory and topological indices, which are mathematical quantities derived from molecular graphs without empirical measurements. Distance-based indices, including Wiener, Szeged, Mostar, and PI, are investigated for their correlation with various physicochemical properties such as boiling point, enthalpy, flash point, molar refraction, polarizability, and molar volume. The Wiener and Padmakar-Ivan indices are found to be particularly effective in predicting these properties, with the Wiener index showing strong correlations with boiling point, enthalpy, and flash point, and the Padmakar-Ivan index correlating well with molar refraction, polarizability, and molar volume. The study also compares the proposed models with earlier degree-based models, finding that the distance-based models perform better in predicting boiling point, enthalpy, and flash point, while marginally less in predicting molar refraction, polarizability, and molar volume. The findings could be crucial for the development of new TB drugs and vaccines, and the authors suggest further exploration of distance-dependent models for COVID-19 drug molecules.This paper focuses on developing a Quantitative Structure-Property Relationship (QSPR) model using distance-based topological indices for anti-tuberculosis (TB) medications and their physicochemical features. The study employs chemical graph theory and topological indices, which are mathematical quantities derived from molecular graphs without empirical measurements. Distance-based indices, including Wiener, Szeged, Mostar, and PI, are investigated for their correlation with various physicochemical properties such as boiling point, enthalpy, flash point, molar refraction, polarizability, and molar volume. The Wiener and Padmakar-Ivan indices are found to be particularly effective in predicting these properties, with the Wiener index showing strong correlations with boiling point, enthalpy, and flash point, and the Padmakar-Ivan index correlating well with molar refraction, polarizability, and molar volume. The study also compares the proposed models with earlier degree-based models, finding that the distance-based models perform better in predicting boiling point, enthalpy, and flash point, while marginally less in predicting molar refraction, polarizability, and molar volume. The findings could be crucial for the development of new TB drugs and vaccines, and the authors suggest further exploration of distance-dependent models for COVID-19 drug molecules.