<p>Sand-sliding slope instability poses difficulties for construction in the Tibetan Plateau and adjacent areas. At present, a simple and reliable method for the engineering evaluation of the water–sand flow susceptibility of such slopes is not available. Therefore, herein, classical machine learning algorithms—gray relation analysis, random forest, and entropy weight method—were employed to evaluate the index weights for the water–sand flow susceptibility of sand-sliding slopes. This analysis was performed on field investigation data from 51 sand-sliding slopes. Building on debris flow susceptibility evaluation methodologies, a dedicated evaluation model for the susceptibility of sand-sliding slopes to water–sand flow was developed. By integrating qualitative field criteria for water–sand flow with a four-tier classification system (extremely susceptible, moderately susceptible, mildly susceptible, and non-susceptible), a quantitative susceptibility evaluation criterion was established through comparative statistical analysis. Our model exhibited an accuracy rate of 92.31% for water–sand flow susceptibility estimation on validation testing with 13 field engineering samples. The practical applicability of the model was further validated on 34 samples under an actual engineering project, achieving an enhanced accuracy of 94.12% with high safety performance. These results confirm the reliability, practical applicability, and generalizability of the proposed methodology.</p>