Estimating event-free survival after a hematopoietic cell transplant is not straightforward. Each patient has unique factors that interact and affect the result. Traditional survival models like the Cox proportional hazards model have limitations. They cannot easily capture nonlinear patterns in the data. They also fail to model complex interactions between features. As a result, their predictive power is often restricted. We propose a transformer-based framework for EFS prediction in hematopoietic cell transplantation (HCT) patients. It is built using the Feature Tokenizer-Transformer (FT-Transformer) architecture. The design focuses on structured clinical data. The model processes 35 categorical variables through embedding layers. It also handles 22 continuous features using optimized linear projections. Multi-head self-attention then learns key relationships between the features automatically. In our approach, we obtained a validation concordance index of 0.689. This score is 6.6% higher than traditional models. Survival analysis using Kaplan-Meier curves confirms distinct group separation (p < 0.001). Attention-based explanations reveal influential clinical attributes. As a result, our solution improves model transparency in medical settings. This study demonstrates the value of transformer models for survival analysis. Their ability to provide patient-specific risk estimates makes them useful in transplant medicine.