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