Although venous thromboembolism (VTE) is an important treatment and disease-related complication in myeloma, a validated risk prediction model including disease-specific variables such as cytogenetics or tumor burden is lacking. The aim of this study was to develop a new risk prediction model for VTE in the context of modern antimyeloma therapy. All consecutive patients diagnosed at the Cleveland Clinic between 2008 and 2018 and with available data on baseline candidate risk factors constituted the derivation cohort. The primary outcome was VTE (deep venous thrombosis/pulmonary embolism) within 1 year of treatment initiation. A multivariable model was used, and weights were derived from subdistribution hazard ratios to construct a risk score. The model was validated both by internal bootstrap validation and in an external validation cohort. The derivation cohort consisted of 783 patients. A 5-component risk prediction tool, named the PRISM score, was developed, including the following variables: prior VTE, prior surgery, immunomodulatory drug use, abnormal metaphase cytogenetics, and Black race. The c-statistic of the model was 0.622 (95% confidence interval [CI], 0.567-0.674). The model stratified patients into low, intermediate, and high risk, with 12-month cumulative VTE incidence of 2.7%, 10.8%, and 36.5%, respectively. Risk of VTE increased significantly with increasing score in both the derivation and the external validation data sets, with a subdistribution hazard ratio per 1-point increase of 1.28 (95% CI, 1.19-1.39; P < .001) and 1.23 (95% CI, 1.07-1.41; P = .004) respectively. Although the PRISM score can guide clinicians in identifying patients at a high risk of VTE, additional external validation is necessary for incorporation into routine clinical practice.