In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and \(R^{2}\) score.