Remote sensing technology has been increasingly applied in soil quality monitoring across various regions to address environmental challenges. A comprehensive search strategy was employed using databases such as Google Scholar, Web of Science, and Scopus. Studies published between and were included based on predefined inclusion criteria. The analysis revealed that remote sensing can accurately map soil quality patterns with a precision rate of up to 85% in degraded areas compared to ground-based measurements. Remote sensing offers a cost-effective and scalable solution for monitoring soil health, particularly in regions lacking extensive field data. Investment should be directed towards developing localized remote sensing models tailored to specific agricultural contexts in northern Kenya. remote sensing, soil quality, agriculture, northern Kenya, precision mapping Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.