Heart disease remains a significant global health concern, and accurate prediction of heart attack risk is crucial for early intervention and prevention. In this study, we employ a diverse set of regression models to predict heart attack risk based on a dataset collected from Kaggle, comprising ten relevant features with a continuous target variable. We evaluate the performance of nine regressor models using traditional regression metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Our comprehensive analysis covers a range of models, from traditional linear regressions to advanced ensemble methods, highlighting their strengths and weaknesses in predicting heart attack risk. Furthermore, we visually depict the model predictions against actual values to provide an intuitive understanding of their performance. The results reveal that certain regressor models exhibit superior predictive capabilities, while others may require further refinement. This research not only provides valuable insights into the application of regression models for heart attack risk prediction but also emphasizes the importance of proper model selection, hyperparameter tuning, and data preprocessing. In conclusion, our study contributes to the ongoing efforts in the field of cardiovascular health by offering a thorough examination of regression models for heart attack risk prediction. The findings serve as a foundation for improving the accuracy and reliability of risk assessment tools, ultimately aiding healthcare practitioners in making more informed decisions and potentially saving lives.