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