Transforming linear regression with AI-driven enhancements for superior forecasting robustness and interpretability

This is one of the first regression machines that data scientists will encounter because it is easier and easier to interpret. It suffers from complex, nonlinear and high dimensional data. In the context of finance, healthcare and climate domains, this study suggests a hybrid machine learning framework combining Adam, RMSProp, XGBoost, SVMs, and neural networks to significantly improve the regression performance. MSE, R2 and efficiency metrics are used to analyse real world datasets. The financial forecasting MSE is reduced by 18% and the healthcare R2 improved by 22%. Noisy data was easier to deal with for climate models. Because they preserved interpretability, features were indicated by SHAP values. Blending classical statistics with modern AI transforms the problem into more accurate, scalable, and interpretable models, providing robust solutions for todays complex data challenges, which are proven.