China's energy structure exhibits a pattern dominated by coal, with comparatively lower reliance on oil and natural gas. As a unique coal resource, oil-rich coal plays a crucial role in alleviating the nation's oil and gas shortages. Coal tar yield serves as the primary criterion for identifying oil-rich coal and is typically determined using the Grignard distillation test. However, research on evaluating coal tar yield through geophysical methods remains limited. To develop a predictive approach based on logging parameters, taking the Santanghu Coalfield Tiaohu Mining Area in Xinjiang as an example, a comprehensive database integrating coal tar yield, coal quality parameters, and logging data was compiled. The dataset was constructed through 26 industrial and elemental analyses, Grignard distillation tests, and 13 logging measurements, including spontaneous potential, natural gamma, and compensated density. Statistical techniques were applied to identify key factors influencing coal tar yield in the study area, determine the geological parameters associated with coal tar production, and develop a predictive model integrating multiple regression analysis and random forest regression. The findings indicate that the primary geological factors affecting coal tar yield include coal ash content, the H/C, and the mirror inert ratio, while the most influential logging parameters are spontaneous potential, natural gamma, and compensated density. Moreover, a multiple regression model was developed to estimate coal quality parameters based on logging data, while a random forest regression model was applied to predict coal tar yield using coal quality parameters. The research innovatively integrates these two models for coal tar yield prediction, achieving an accuracy of 89%. The findings offer a novel approach for forecasting oil-rich coal tar yield, enhance the precision of coal tar yield evaluation models, and provide valuable technical support for assessing geological reserves of coal tar.