Gradient Boosted Mixed Models (GBMixed) represent a novel methodology designed for the analysis of clustered data by integrating the capabilities of linear mixed models with gradient boosting techniques. This approach enhances both flexibility and predictive accuracy, allowing for more nuanced modeling of complex datasets. A key feature of GBMixed is its ability to jointly estimate mean and variance components, which addresses the challenge of uncertainty quantification often encountered in clustered data analysis. By combining these strengths, GBMixed offers improved performance over traditional methods in handling hierarchical or grouped data structures. The framework’s design specifically targets the difficulties in modeling variance heterogeneity alongside fixed and random effects. This innovation has been positively proposed as an effective tool in the field of machine learning and statistical modeling. Overall, GBMixed provides a promising direction for researchers seeking robust and adaptable models for clustered data scenarios.