An advanced assessment methods together with procedures to restore the degraded soil quality remain an urgent worldwide need. This research utilizes geographic information systems (GIS) and machine learning (ML) to create an effective and precise system which draws restoration zones for soil quality. Soil depth prediction through key environmental factors in Washim district, Maharashtra, depends on the combination of random forest (RF), XGBoost, and Cubist models within this study. Models received training input from an extensive collection of topographic, remote sensing alongside soil property variables which enabled validation assessments. Soil depth predictions showed elevation and slope variables as the primary contributors (0.28 and 0.22, respectively) which surpassed NDVI and soil type and land-use factors. XGBoost delivered exceptional results with R2 = 0.97 and RMSE = 6.24 cm but RF maintained a combination of R2 = 0.91 accuracy together with generalizing performance. The predictive models developed by Cubist showed the lowest level of reliability based on R2 = 0.58. The study showed that valleys possessed deeper soils exceeding 120 cm depth while uplands contained shallower soil layers totaling only 45 cm depth. A combined approach of ML algorithms together with GIS enables policymakers as well as farmers and environmental managers to execute targeted soil restoration approaches. Through this approach, scientists can improve land-use planning operations and precision agriculture practices as well as sustainable soil management leading to worldwide conservation achievements.