<p>A set of ten eigenvalue-based topological indices are investigated in this study to characterize the topology of networks in the context of kidney cancer drugs. These indices play a crucial role in quantitative structure–activity relationship research, offering insights into the physicochemical attributes of chemical compounds. The article focuses on utilizing these indices to provide researchers with a deeper comprehension of the physical attributes and chemical interactions among kidney cancer medications. By applying eigenvalue-based topological indices, the analysis covers various properties such as boiling point, enthalpy, molar volume, flash point, molar refraction, density, polar surface area, and molar weight of kidney cancer medicines. To assess the predictive capability of these indices, regression models including linear, quadratic, cubic and logarithmic models are employed. Through correlation coefficient analysis, the study establishes a connection between estimated values and empirical data for kidney cancer drugs. This innovative theoretical approach has the potential to enable chemists and healthcare professionals to predict the properties of kidney cancer drugs without the need for extensive testing, thereby streamlining drug development processes.</p>