Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are a class of medications that are used for different therapeutic uses.They effectively alleviate pain, reduce inflammation, and manage fever.These drugs are available in various forms.NSAIDs are prescribed by healthcare professionals to address a wide range of symptoms, from headaches and dental pain to conditions like arthritis and muscle stiffness.In this work, we use ve-degree-based reducible topol. descriptors in quant. structure-property relationship (QSPR) anal. to estimate the physicochem. properties of NSAIDs.In the first step, we have developed a MAPLE-based code to compute the reducible ve-degree-based topol. descriptors of NSAIDs.Then, a linear regression model was used to estimate four physicochem. properties of seventy NSAIDs.It has been observed that two physicochem. properties, namely Mol. Weight and Complexity show a very strong correlation with the reducible ve-degree-based topol. descriptors.For both cases, the value of correlation coefficient is greater than 0.9.Finally, quadratic and cubic regression models were constructed, and a comparative anal. with these models is presented.These results may help enhance the understanding of NSAIDs medication structures and aid in predicting their pharmacol. activity.