This study modeled root length density of wheat as a nonlinear function of bulk density, penetration resistance, and soil depth. Field experiments on alluvial soils subjected to tractor-induced compaction generated datasets for model development. A conventional log-linear model and a deep neural network were compared, and global sensitivity analysis was performed to assess parameter influence. Results showed that the deep neural network provided superior predictive accuracy over the analytical model, effectively capturing nonlinear and depth-dependent interactions. Sobol sensitivity analysis identified soil depth as the most influential factor affecting root length density, followed by penetration resistance and bulk density. The study concludes that integrating soil depth with machine learning approaches enhances root length density prediction and interpretation, emphasizing the significance of soil compaction parameters in guiding precision agriculture and sustainable soil management practices.