A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and proximity potential model (CPPM) is developed to investigate $\alpha$-particle preformation factors $P_{\alpha }$ and their impact on $\alpha$-decay half-lives. The TabPFN model, trained on 498 nuclei, accurately learns the relationship between nuclear-structure properties and $P_{\alpha }$, achieving a root-mean-square deviation of ${\sigma }_{\mathrm{rms}}=0.211$. The predicted factors reveal clear odd-even staggering and shell-closure effects, and exhibit linear correlations with both $Q_{\alpha }^{-1/2}$ and the fragmentation potential $V_{\mathrm{frag}}$. When incorporated into CPPM calculations, the machine-learning-based $P_{\alpha }$ values significantly improve half-life predictions. Similar improvements are also obtained when deformation effects are included in the potential barrier description. The capability of the model is further demonstrated through predictions for superheavy nuclei ($Z=117–120$), suggesting $N=184$ as a potential neutron magic number.

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• Alpha decay
• Lifetimes & widths
• Nuclear reactions
• Artificial neural networks