This research advances joint modeling and personalized scheduling for HIV and TB by incorporating censored longitudinal outcomes in multivariate joint models, providing a more flexible and accurate approach for complex data scenarios. Inspired by the SAPiT study, we deviate from standard model selection procedures by using super learning techniques to identify the optimal model for predicting future events in event-free subjects. Specifically, the Integrated Brier score and Expected Predictive Cross-Entropy (EPCE) identified the multivariate joint model with the parameterization of the area under the longitudinal profiles of CD4 count and viral load as optimal and strong predictors of death. Integrating this model with a risk-based screening strategy, we recommend extending intervals to 10.3 months for stable patients, with additional measurements every 12 months. For patients with deteriorating health, we suggest a 3.5-month interval, followed by 6.2 months, and then annual screenings. These findings refine patient care protocols and advance personalized medicine in HIV/TB co-infected individuals. Furthermore, our approach is adaptable, allowing adjustments based on patients' evolving health status. While focused on HIV/TB co-infection, this method has broader applicability, offering a promising avenue for biomarker studies across various disease populations and potential for future clinical trials and biomarker-guided therapies.