Deep　venous　thrombosis　(DVT)　is　a　leading　cause　of　cardiovascular-related　mortality,　with　an　increasing　incidence　in　elderly　patients.　However,　existing　risk　assessment　tools　remain　limited　for　this　population.　This　study　aimed　to　develop　and　validate　machine　learning　(ML)-based　models　for　predicting　DVT　risk　in　elderly　patients.　We　retrospectively　analyzed　data　from　1226　elderly　patients　discharged　from　the　cardiovascular　surgery　department　between　January　2022　and　December　2023.　Risk　factors　were　identified　using　the　least　absolute　shrinkage　and　selection　operator　(LASSO),　and　seven　ML　models　were　subsequently　trained　on　the　selected　features.　Optimal　hyperparameters　for　each　model　were　selected　through　grid　search　with　ten-fold　cross-validation.　Logistic　regression　(LR)　and　random　forest　(RF)　demonstrated　the　best　performance,　with　areas　under　the　receiver　operating　characteristic　curve　(AUCs)　of　0.835　and　0.819,　respectively.　SHapley　Additive　exPlanations　(SHAP)　revealed　swelling,　pain,　albumin　(ALB),　and　D-dimer　as　key　predictors.　These　models　may　facilitate　accurate　risk　stratification　in　elderly　patients　and　provide　clinical　decision　support　through　an　interactive　web-based　tool.