Human age prediction in forensic science is an increasingly crucial endeavor, as accurate age estimation can significantly aid casework and investigations. Recent studies show that DNA methylation, combined with advanced regression models, provides a promising avenue for precise human age determination. Massive Parallel Sequencing (MPS), considered in some studies as a separate high-throughput approach, can capture extensive methylation data but often requires substantial computational and financial resources compared to the regression models discussed. This paper reviews existing literature on DNA methylation–based and image-based age estimation methods, highlighting their gaps and limitations for forensic use. Further, we clarify how the Gradient Boosting Regressor specifically addresses limitations like non-linear relationships and outliers by adaptively weighing each feature over multiple boosting rounds. We present a comparative evaluation of several regression models, including Linear Regression, Support Vector Regression, Bayesian Ridge, Random Forest Regression, Massive Parallel Sequencing, Hybrid Classification–Regression methods, and the proposed Gradient Boosting Regressor. Our focus is on the ways in which combining DNA methylation biomarkers with Gradient Boosting Regressor addresses key gaps in forensic practice. We examine model accuracy through Mean Absolute Deviation (MAD) and Precision (%) and conclude that DNA methylation with Gradient Boosting Regressor demonstrates robust performance and reliability for forensic age estimation. The integration of non-invasive sampling methods and longitudinal methylation dynamics enhances practical applicability. This paper’s findings illuminate the potential for integrating epigenetic data and machine learning in future forensic applications.