Sepsis, a life-threatening disease, requires accurate prediction methods to facilitate timely interventions and enhance patient outcomes. This paper presents an integration of deep transformer model with multimodal healthcare data for sepsis prediction. Sepsis, a life-threatening condition, demands rapid and precise prediction for effective interventions and improved patient outcomes. This study presents a groundbreaking solution, the Deep Transformer-Based Model for Sepsis Prediction with Multimodal Health Data. Leveraging transformer-based architectures, this model integrates diverse patient data, including clinical notes, vital signs, and lab results, to enhance sepsis detection accuracy. By fusing deep learning techniques with multimodal data, the model achieves superior predictive capabilities. The proposed approach not only advances sepsis prediction but also holds promise for real-time clinical decision-making, leading to data-driven critical care. The experimentation and evaluation, reveal that the proposed deep transformer-based model for sepsis prediction is more efficient than the existing methods.