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, utilizes 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 162746 sources with an average estimated accuracy of ~86 per cent, based on confusion matrices. We identified 4985 YSOs across the disc 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 HII regions were recovered with ~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 [Halpha]/[24um] and [250um]/[500um] to estimate the relative evolutionary status of all M 33 SFRs. Masses were estimated for each YSO ranging from 6-27M_{sun}_. Using these masses, we estimate star formation rates based on direct YSO counts of 0.63M_{sun}_/yr in M 33's SFRs, 0.79+/-0.16M_{sun}_/yr in its centre and 1.42=/-0.16M_{sun}_/yr globally.