Organ donation is a multifaceted process involving the donor’s family, medical professionals, and significant financial costs, all aimed at dramatically improving the recipient’s quality of life. However, if an organ is deemed unsuitable for transplantation, the entire process may become ineffective. Assessing the viability of an organ prior to extraction is a critical step in ensuring the success of the transplant. This study explores the potential of AI models to predict organ suitability for donation based on the donor’s clinical records prior to extraction. Using data from over 2,700 donors between 2015 and 2023 from medical centers within the Catalan Organization of Transplants (OCATT), we compared the performance of several machine learning algorithms, including Decision Trees (DT), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machines (SVM). The RF model exhibited the highest accuracy in predicting organ viability, aligning closely with expert medical assessments, especially for kidneys and livers. Additionally, we analyzed the key features the model relies on to predict organ viability, providing deeper insights into organ health through medical records. Our findings lay the groundwork for developing a data-driven tool that could assist clinicians in evaluating organ viability more effectively, ultimately improving the transplant process.