Land subsidence and collapsed pipes are considered among geomorphological hazards, causing significant damages annually in the form of direct and indirect costs. These hazards lead to notable changes in the landscape, land degradation, soil and water losses, and regional erosion and sedimentation. Consequently, the effective management of these hazards and the determination of relationships between their environmental factors for quantitative susceptibility assessment are of utmost importance. A trustworthy evaluation depends on the quality of available data and the selection of appropriate analytical and modeling methods. Given that no comprehensive study on land subsidence and collapsed pipes in Razavi Khorasan Province has been conducted so far and considering that the land degradation resulting from plain land subsidence and collapsed pipes are among the primary threatening hazards for the country and the province, particularly in recent years, the use of proper analytical and modeling methods for comprehensive and integrated management seems essential. This research was conducted using high-resolution satellite imagery in Razavi Khorasan Province. In this study, topographical and hydrological feature maps were prepared using a digital elevation model based on the boundaries of Razavi Khorasan Province. Physical and chemical tests were conducted on 624 soil samples collected throughout the province, and their raster maps were produced. Data pertaining to vegetation cover, land use maps, geology, and regional precipitation were also prepared and used as inputs for the models. The spatial locations of land subsidence and collapsed pipes across the province were identified in subsequent phases. Following this, using statistical and data mining methods, spatial modeling of the land subsidence and collapsed pipes was performed, and the best regional model for their evaluation was chosen. The AUC numerical value for both the support vector machine (SVM) and maximum entropy (ME) models ranges between 0.8 and 0.9, indicating an excellent evaluation of the models used in zoning the land subsidence. Ultimately, the ability to recognize the behavior and formation conditions of these hazards, to identify areas with greater susceptibility, to present a risk management model for land subsidence and collapsed pipes, and to distinguish critical and susceptible areas for land subsidence and collapsed pipes, along with their control methods, was provided. Notably, the SVM algorithm demonstrated superior efficacy in this study. The insights derived from identifying erosional structures of collapsed pipes and land subsidence and understanding their spatial interrelationships offer a robust foundation for devising timely and strategic management interventions in affected domains.