Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disorder (BD) who have elevated risks of cardiometabolic conditions and premature mortality. Therefore, we aimed to: 1) develop a five-year CVD risk prediction model in this population by using nationwide register data from Sweden, 2) investigate whether the performance improved when we considered additional risk factors, including psychiatric comorbidity, psychotropic medication, and socio-demographic variables, compared to using established CVD risk factors only, and 3) whether machine learning approach provided improvements compared to standard logistic regression models. We followed 33,933 persons with BD aged 30-82 years old, without previous CVD, from the date of BD diagnosis registered between 2007-2014, for up to five years. The logistic regression model containing only established risk factors yielded an area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval 0.74-0.78) in the test dataset, while the logistic regression model and the best performing machine learning model including additional predictors yielded similar results (AUC was 0.77 (0.75, 0.79) in both models). The performance of logistic regression models slightly improved with additional predictors when continuous risk scores were used. In conclusion, standard logistic regression and established CVD risk factors may be sufficient to predict CVD in individuals with BD when using population register-based data from Sweden. External validation across diverse healthcare settings and rigorous assessment of clinical impact will be crucial next steps before implementing these models in clinical practice.