We explore the top quark flavor-changing four-fermion interactions ($tuee$ and $tcee$) with scalar, vector, and tensor structures using machine learning models to analyze trilepton processes at the LHC. The study is performed using $t\overline{t}$ and $tW$ processes, where a top quark decays into $u/c+e^{+}+e^{-}$. The analysis incorporates both reducible and irreducible backgrounds while accounting for realistic detector effects. The dominant backgrounds for these trilepton signatures arise from $t\overline{t}$ production, single top quark production in association with $V$, and $VV$ production (where $V=W$, $Z$). These backgrounds are significantly reduced using machine-learning-based classification models, which optimize event selection and improve signal sensitivity. For an integrated luminosity of $3000{\mathrm{fb}}^{-1}$ at the LHC, we find that the expected 95% confidence level (CL) limits on the scale of four-fermion flavor-changing neutral current interactions reach $\mathrm{\Lambda }\le 5.5\mathrm{TeV}$ for $tuee$ and $\mathrm{\Lambda }\le 5.7\mathrm{TeV}$ for $tcee$ in the $t\overline{t}$ channel, and $\mathrm{\Lambda }\le 1.9\mathrm{TeV}$ ($tuee$) and $\mathrm{\Lambda }\le 2.0\mathrm{TeV}$ ($tcee$) in the $tW$ channel. We also provide an interpretation of our EFT analysis in the context of a specific $Z^{\prime }$ model, illustrating how the derived constraints translate into bounds on the parameter space of a heavy neutral gauge boson mediating flavor-changing interactions.

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• Flavor changing neutral currents
• Hadron colliders
• Top quark
• Supervised learning