  Long-lived particles (LLPs) provide an unambiguous signal for physics beyond
the Standard Model (BSM). They have a distinct detector signature, with decay
lengths corresponding to lifetimes of around nanoseconds or longer. Lepton
colliders allow LLP searches to be conducted in a clean environment, and such
searches can reach their full physics potential when combined with machine
learning (ML) techniques. In the case of LLPs searches from Higgs decay in
$e^+e^-\to ZH$, we show that the LLP signal efficiency can be improved up to
99% with an LLP mass around 50 GeV and a lifetime of approximately $1$
nanosecond, using deep neural network based approaches. The signal sensitivity
for the branching ratio of Higgs decaying into LLPs reaches $1.2 \times
10^{-6}$ with a statistics of $4 \times 10^{6}$ Higgs.
