Matrix spillover remains a persistent issue in flow cytometry analysis, influencing the reliability of experimental results. Recently, machine learning algorithms have emerged as potential tools to mitigate matrix spillover effects. AI-mediated approaches leverage sophisticated algorithms to identify spillover events and adjust for their impact on