  Majorana zero modes (MZMs), emerging as exotic quasiparticles that carry
non-Abelian statistics, hold great promise for achieving fault-tolerant
topological quantum computation. A key signature of the presence of MZMs is the
zero-bias peaks (ZBPs) from tunneling differential conductance. However, the
identification of MZMs from ZBPs has faced tremendous challenges, due to the
presence of topological trivial states that generate spurious ZBP signals. In
this work, we introduce a machine-learning framework that can discern MZM from
other signals using ZBP data. Quantum transport simulation from tight-binding
models is used to generate the training data, while persistent cohomology
analysis confirms the feasibility of classification via machine learning. In
particular, even with added data noise, XGBoost classifier reaches $85\%$
accuracy for 1D tunneling conductance data and $94\%$ for 2D data incorporating
Zeeman splitting. Tests on prior ZBP experiments show that some data are more
likely to originate from MZM than others. Our model offers a quantitative
approach to assess MZMs using ZBP data. Furthermore, our results shed light on
the use of machine learning on exotic quantum systems with
experimental-computational integration.
