  We present a supervised machine learning classification of stellar
populations in the Local Group spiral galaxy M\,33. The Probabilistic Random
Forest (PRF) methodology, previously applied to populations in NGC\,6822,
utilises both near and far-IR classification features. It classifies sources
into nine target classes: young stellar objects (YSOs), oxygen- and carbon-rich
asymptotic giant branch stars, red giant branch and red super-giant stars,
active galactic nuclei, blue stars (e.g. O-, B- and A-type main sequence
stars), Wolf-Rayet stars and Galactic foreground stars. Across 100
classification runs the PRF classified 162,746 sources with an average
estimated accuracy of $\sim$\,86\,per\,cent, based on confusion matrices. We
identified 4985 YSOs across the disk of M\,33, applying a density-based
clustering analysis to identify 68 star forming regions (SFRs) primarily in the
galaxy's spiral arms. SFR counterparts to known H\,{\sc ii} regions were
recovered, with $\sim$\,91\,per\,cent of SFRs spatially coincident with giant
molecular clouds identified in the literature. Using photometric measurements,
as well as SFRs in NGC\,6822 with an established evolutionary sequence as a
benchmark, we employed a novel approach combining ratios of
[H$\alpha$]$/$[24$\mu$m] and [250$\mu$m]$/$[500$\mu$m] to estimate the relative
evolutionary status of all M\,33 SFRs. Masses were estimated for each YSO
ranging from 6\,$-$\,27\,M$_\odot$. Using these masses, we estimate star
formation rates based on direct YSO counts of 0.63\,M$_\odot$\,yr$^{-1}$ in
M\,33's SFRs, 0.79\,$\pm$\,0.16\,M$_\odot$\,yr$^{-1}$ in its centre and
1.42\,$\pm$\,0.16\,M$_\odot$\,yr$^{-1}$ globally.
