Uncontrolled release of flammable gases and liquids can lead to the formation of flammable vapor clouds. When their concentrations are above the lower flammable limit (LFL), or 1/2 LFL for conservative evaluation, fires and explosions can happen in the presence of an ignition source. The objective of this work is to develop highly efficient consequence models to precisely predict the downwind maximum distance, minimum distance, and maximum vapor cloud width within the flammable limit. In this work, the novel methodology named quantitative property-consequence relationship (QPCR) is proposed and constructed to precisely predict flammable dispersion consequences in a machine learning and data-driven manner. A flammable dispersion database consisting of 450 leak scenarios of 41 flammable chemicals was constructed using PHAST simulations. A state-of-art machine learning regression method, the extreme gradient boosting algorithm, was implemented to develop models. The coefficient of determination (R-2) and root-mean-square error (RMSE) were calculated for statistical assessment, and the developed QPCR models achieved satisfactory predictive capabilities. All developed models had high precision, with the overall RMSE of three models being 0.0811, 0.0741, and 0.0964, respectively. The developed QPCR models can be used to obtain instant flammable dispersion estimations for other flammable chemicals and mixtures at much lower computational costs.