The VISTA Variables in the Via Lactea (VVV) survey and its extension, have been monitoring about 560 square degrees of sky centred on the Galactic bulge and inner disc for nearly a decade. The photometric catalogue contains of order 10$^9$ sources monitored in the K$_s$ band down to 18 mag over hundreds of epochs from 2010-2019. Using these data we develop a decision tree classifier to identify microlensing events. As inputs to the tree, we extract a few physically motivated features as well as simple statistics ensuring a good fit to a microlensing model both on and off the event amplification. This produces a fast and efficient classifier trained on a set of simulated microlensing events and catacylsmic variables, together with flat baseline light curves randomly chosen from the VVV data. The classifier achieves 97 per cent accuracy in identifying simulated microlensing events in a validation set. We run the classifier over the VVV data set and then visually inspect the results, which produces a catalogue of 1,959 microlensing events. For these events, we provide the Einstein radius crossing time via a Bayesian analysis. The spatial dependence on recovery efficiency of our classifier is well characterised, and this allows us to compute spatially resolved completeness maps as a function of Einstein crossing time over the VVV footprint. We compare our approach to previous microlensing searches of the VVV. We highlight the importance of Bayesian fitting to determine the microlensing parameters for events with surveys like VVV with sparse data.
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