Massive stars play a significant role in different branches of astronomy, from shaping the processes of star and planet formation to influencing the evolution and chemical enrichment of the distant universe. Despite their high astrophysical significance, these objects are rare and difficult to detect. With Gaia's advent, we now possess extensive kinematic and photometric data for a significant portion of the Galaxy that can unveil, among others, new populations of massive star candidates. In order to produce bonafide bright (G magnitude <12) massive-star candidate lists (threshold set to spectral type B2 or earlier but with slight changes in this threshold also explored) in the Milky Way subject to be followed up by future massive spectroscopic surveys, we developed a Gaia DR3 plus literature data based methodology. We trained a balanced random forest (BRF) with the spectral types from the Skiff compilation as labels. Our approach yields a completeness of ~80% and a purity ranging from 0.51+/-0.02 for probabilities between 0.6 and 0.7, up to 0.85+/-0.05 for the 0.9-1.0 range. To externally validate our methodology, we searched for and analyzed archival spectra of moderate- to high-probability (p>0.6) candidates that are not contained in our catalog of labels. Our independent spectral validation confirms the expected performance of the BRF, spectroscopically classifying 300 stars as B3 or earlier (due to observational constraints imposed on the B0-3 range), including 107 new stars. Based on the most conservative yields of our methodology, our candidate list could increase the number of bright massive stars by ~50%. As a byproduct, we developed an automatic methodology for spectral typing optimized for LAMOST spectra, based on line detection and characterization that guides a decision path.