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Objective: To investigate the association between triglyceride-glucose (TyG)-body shape index (BSI) and all-cause and cause-specific mortality in the elderly population. Methods: Data were derived from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018, and a total of 8 093 adults aged ≥60 years were included. Fasting blood glucose, triglycerides, waist circumference, height, and body mass index were used to calculate TyG, BSI, and TyG-BSI (the product of TyG and BSI). Participants were divided into four groups based on TyG-BSI quartiles. Outcomes included all-cause, cardiovascular, and non-cardiovascular mortality. Cox proportional hazards models and restricted cubic spline analyses were employed to assess the association between TyG-BSI and mortality risk. Survival differences across TyG-BSI quartiles were compared using Kaplan-Meier curves. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of TyG-BSI for mortality, and the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to assess its incremental predictive value over traditional metabolic indices (TyG, TyG-waist circumference, and TyG-waist-to-height ratio). Results: The average age of the study population was 69.6 years (95%CI 69.3-69.8), with 46.0% (95%CI 44.9%-47.1%) males. Over a median follow-up of 8.3 years, 2 874 (35.5%) all-cause deaths occurred, including 949 (11.7%) cardiovascular deaths and 1 925 (23.8%) non-cardiovascular deaths. Cox regression analysis showed that after adjusting for age, sex, race, and other covariates, compared with the lowest TyG-BSI quartile, the highest TyG-BSI quartile was associated with significantly higher risks of all-cause mortality (HR=1.34, 95%CI 1.14-1.57), cardiovascular mortality (HR=1.41, 95%CI 1.08-1.85), and non-cardiovascular mortality (HR=1.31, 95%CI 1.07-1.61). Restricted cubic spline analysis revealed a linear positive association between TyG-BSI and mortality risk. Kaplan-Meier survival curves demonstrated a decreasing survival trend with increasing TyG-BSI quartiles. The ROC analysis yielded area under the curve values of 0.577, 0.591, and 0.550 for predicting all-cause, cardiovascular, and non-cardiovascular mortality, respectively. TyG-BSI provided incremental predictive value over traditional metabolic indices (both NRI and IDI0). Conclusions: TyG-BSI is positively associated with all-cause and cause-specific mortality in elderly populations, offering incremental predictive value for mortality beyond that of traditional metabolic indicators. While TyG-BSI can be used as an additional indicator for the initial screening of mortality risk in the elderly, its discriminative performance is limited when used alone and it should be combined with traditional clinical risk factors for comprehensive assessment.