Liver transplantation is very important treatment for end-stage liver disease. However, organ shortage and complex donor-recipient matching decisions requires advanced computational tools for optimizing organ allocation strategies and predicting post-transplant outcomes. In spite of significant improvements in artificial intelligence, the clinical integration of machine learning models is hindered by their opacity, limiting clinician trust and regulatory approval. Current liver transplantation relies on traditional scoring systems that relies static, generalized variables, failing to capture patient-specific interactions and complex patterns influencing outcomes (Spann et al., 2025). This research reviews an integrated explainable deep learning model that combines advanced neural architectures with interpretability mechanisms to enhance real-time clinical decision support across the transplantation continuum. In this, we provide a comprehensive survey of AI-driven transplant decision support system integrates various patient and donor data sources, harmonizing them into a multimodal feature space (Mohapatra et al., 2025). It supports compatibility prediction and post-transplant survival rating. The architecture ensures clinical utility, explainability, governance, and regulatory compliance throughout the model sequence. Key variables include multiple inputs, such as EHRs/clinical notes, imaging, genomics, laboratory results, donor registries, and consent metadata.