The demand for efficient prediction methods to evaluate and treat post-COVID disorders has increased due to the COVID-19 epidemic. This work presents a novel strategy for anticipating and addressing these health risks that makes use of transformer-based models. To improve the disease prognosis accuracy, this study integrates detailed tabular data and X-ray images of chest, who pretentious with COVID-19.A hybrid transformer model that incorporates the most recent advancements helps to predict the course of the disease. This approach makes it possible for the model to successfully adjust to each patient’s distinctive characteristics and disease progression. Preliminary findings indicate that the proposed approach demonstrates promising results in accurately prognosing post-COVID diseases. Integrating diverse datasets significantly improves the model’s predictive capabilities and treatment efficacy, allowing for tailored recommendations that align with individual patient needs. The Vision Token Transformer (ViToT) architecture is a Hybrid transformer, which contains Vision Transformer in order to train a model with chest X-ray (CXR) images and Feature Tokernizer transformer in order extract features from tabular data to capable of recognizing and categorizing key patterns in CXR images while also extracting optimal features from tabular data. Late fusion technique is applied to combine an extracted feature which leads to cardiovascular disorder. Across 150 training epochs, the model demonstrated a robust performance, achieving a final accuracy of 97.6%, indicates that the model generalizes extremely effectively. This approach highlights the potential of hybrid-based transformer algorithms to enhance post-pandemic healthcare management. In the post-COVID period, it enables healthcare providers to improve patient care by facilitating early disease prognosis.