This paper explores the development and evaluation of a multimodal system for survival prediction in clinical settings, leveraging both structured electronic health records and unstructured clinical notes. The core objective is to enhance the accuracy and reliability of survival predictions in Intensive Care Units by integrating diverse data types through advanced machine learning models. The system combines the novel architecture of Tabular Transformers, adapted to process structured data such as patient demographics, medical history, and diagnoses, with Multi-Layer Perceptrons for text embeddings obtained from BioClinicalBERT, a specialized model for clinical narratives. These models' integration aims to capture the complex and multifaceted nature of patient profiles, thereby improving prediction performance. The finalized system, a Logistic Regression instance that aggregates the obtained predictions, demonstrates superior performance on evaluation metrics, highlighting the system's ability to identify high-risk patients. Comprehensive benchmarking against decision trees, standalone MLPs, and various configurations underscored the robustness of the proposed system. This research highlights the transformative potential of multimodal data integration in medical predictive modeling. Thus, medical professionals can guide their efforts and prioritization of patient care, enabling more efficient and targeted allocation of resources during triage.