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Liver disease being a major global health problem, with millions of patients progressing to final-stage liver failure and limited pool of donor organs[6]. Existing models for liver transplant decision-making, such as the MELD score, rely on a small number of biomarkers, do not fully capture disease complexity, and do not integrate imaging or automated donor– recipient matching[7]. This leads to delayed decisions, fragmented workflows, and potential inequities in organ allocation.This paper presents Orgdan, a human-centered, web-based platform that integrates liver risk assessment, automated medical image analysis, and intelligent donor–recipient matching into a single decision-support system optimized for practical clinical use.Orgdan combines three core components: (1) An eight-biomarker risk scoring model built from the Indian Patient Liver Disease (IPLD) dataset using logistic regression-derived weights[3] [5] (2) A lightweight computer vision pipeline for semi- automatic liver segmentation from CT and MRI scans using classical image processing [4] and (3) An AI-assisted donor matching module based on a ternary tree structure and Grey Wolf Optimization (GWO) tuned Random Forest, SVM and XGBoost [14]. Performance was evaluated using risk classification metrics, segmentation overlap scores, donor matching accuracy, and comparison with the MELD score. The biomarker-based risk model achieved an overall accuracy of 92% in classifying patients into low, medium and high liver risk categories, with a low false negative rate for high-risk patients. The liver segmentation module reached a mean Intersection over Union (IoU) of 0.87 and Dice coefficient of 0.93 across 50 CT/ MRI scans, with average processing time of 2.3 seconds per image on a standard workstation. The GWO optimized donor matching framework attained an Area Under the ROC Curve (AUROC) of 0.92, with 85–90% accuracy across tuned models, outperforming the traditional MELD-based baseline (AUROC 0.68). Orgdan demonstrates that a humanized, interpretable, and computationally efficient AI platform can augment clinical decision making in liver disease by bringing together risk scoring, imaging, and donor matching [13]. Compared with MELD, Orgdan uses twice the number of biomarkers, provides transparent explanations for predictions, and automates several currently manual steps. Future work will focus on validation using real transplant registry data, deeper integration into hospital information systems, and enhancement of the imaging module with advanced segmentation networks [11].