Regression models for machine learning are a foundational research area focused on predicting continuous outcomes and understanding relationships between input variables in various domains such as finance, healthcare, IoT, and engineering. Research papers in this domain explore traditional regression techniques like linear regression, polynomial regression, ridge and lasso regression, as well as advanced machine learning-based approaches including support vector regression (SVR), decision tree regression, ensemble methods (random forests, gradient boosting), and deep learning regression models. Key contributions include feature selection and engineering, handling multicollinearity, dealing with missing or noisy data, and improving model generalization and interpretability. Recent studies also address challenges such as high-dimensional datasets, real-time prediction, uncertainty quantification, and integration with edge/fog computing for low-latency decision-making in IoT and smart systems. By leveraging regression models, research in this area aims to provide accurate, interpretable, and scalable predictive solutions for data-driven decision-making.