Matrix spillover remains a challenging issue in flow cytometry analysis, influencing the reliability of experimental results. Recently, machine learning algorithms have emerged as promising tools to mitigate matrix spillover effects. AI-mediated approaches leverage advanced algorithms to quantify spillover events and compensate for their influence