This study explores the use of polynomial functions to enhance the accuracy of machine learning (ML) models in predicting the corrosion inhibition efficiency of pyridine-quinoline compounds. The research aims to address the limitations of traditional experimental methods, which are costly, time-consuming, and resource-intensive. By integrating polynomial functions into Support Vector Regression (SVR), Random Forest (RF), and K-Nearest Neighbors (KNN) models, the study significantly improves the predictive capabilities of these models. The SVR model, in particular, demonstrates the highest accuracy with an R² value of 0.936 and a Root Mean Square Error (RMSE) of 0.093. The results highlight the effectiveness of polynomial functions in enhancing the precision of ML models, making them a robust tool for predicting the corrosion inhibition potential of pyridine-quinoline compounds. This approach contributes to advancing corrosion mitigation strategies and offers valuable insights into the development of more sophisticated predictive models in materials science and corrosion inhibition.This study explores the use of polynomial functions to enhance the accuracy of machine learning (ML) models in predicting the corrosion inhibition efficiency of pyridine-quinoline compounds. The research aims to address the limitations of traditional experimental methods, which are costly, time-consuming, and resource-intensive. By integrating polynomial functions into Support Vector Regression (SVR), Random Forest (RF), and K-Nearest Neighbors (KNN) models, the study significantly improves the predictive capabilities of these models. The SVR model, in particular, demonstrates the highest accuracy with an R² value of 0.936 and a Root Mean Square Error (RMSE) of 0.093. The results highlight the effectiveness of polynomial functions in enhancing the precision of ML models, making them a robust tool for predicting the corrosion inhibition potential of pyridine-quinoline compounds. This approach contributes to advancing corrosion mitigation strategies and offers valuable insights into the development of more sophisticated predictive models in materials science and corrosion inhibition.