Survival analysis helps approximate underlying distributions of time-to-events which in the case of critical care like in the ICU can be a powerful tool for dynamic mortality risk prediction. Extending beyond the classical Cox model, deep learning techniques have been leveraged over the last years relaxing the many constraints of their counterparts from statistical methods. In this work, we propose a novel conditional variational autoencoder-based method called DySurv which uses a combination of static and time-series measurements from patient electronic health records in estimating risk of death dynamically in the ICU. DySurv has been tested on standard benchmarks where it outperforms most existing methods including other deep learning methods and we evaluate it on a real-world patient database from MIMIC-IV. The predictive capacity of DySurv is consistent and the survival estimates remain disentangled across different datasets supporting the idea that dynamic deep learning models based on conditional variational inference in multi-task cases can be robust models for survival analysis.