  In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with
machine learning (ML) to search for binary black hole (BBH) mergers in the
Advanced LIGO-Virgo strain data from the third observing run (O3). We detect,
with equivalent or higher significance, all gravitational-wave (GW) events
previously reported by the standard cWB search for BBH mergers in the third GW
Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five
additional GW candidate events from the catalog that were previously missed by
the standard cWB search. Moreover, we identify three marginal candidate events
not listed in GWTC-3. For simulated events distributed uniformly in a fiducial
volume, we improve the detection efficiency with respect to the standard cWB
search by approximately $20\%$ for both stellar-mass and intermediate mass
black hole binary mergers, detected with a false-alarm rate less than
$1\,\mathrm{yr}^{-1}$. We show the robustness of the ML-enhanced search for
detection of generic BBH signals by reporting increased sensitivity to the
spin-precessing and eccentric BBH events as compared to the standard cWB
search. Furthermore, we compare the improvement of the ML-enhanced cWB search
for different detector networks.
