<div>Boron-dipyrromethene (BODIPY) molecules are widely used as laser dyes and have therefore become a popular research topic within recent decades. Numerous studies have been reported for the rational design of BODIPY derivatives based on their spectroscopic and photophysical properties, including absorption and fluorescence wavelengths (<i>λ</i><sub>abs</sub> and <i>λ</i><sub>fl</sub>), oscillator strength (<i>f</i>), nonradiative pathways, and quantum yield (<i>ϕ</i>). In the present work, we illustrate a theoretical, semi-empirical model that accurately predicts <i>ϕ</i> for various BODIPY compounds based on inexpensive electronic structure calculations, following the data-driven algorithm proposed and tested on the naphthalene family by us [Kohn, Lin, and Van Voorhis, <i>J. Phys. Chem. C.</i> <b>2019</b>, <i>123</i>, 15394]. The model allows us to identify the dominant nonradiative channel of any BODIPY molecule using its structure exclusively and to establish a correlation between the activation energy (<i>E</i><sub>a</sub>) and the fluorescence quantum yield (<i>ϕ</i><sub>fl</sub>). Based on our calculations, either the S<sub>1</sub> → S<sub>0</sub> or <i>L<sub>a</sub></i> → <i>L<sub>b</sub></i> internal conversion (IC) mechanism dominates in the majority of BODIPY derivatives, depending on the structural and electronic properties of the substituents. In both cases, the nonradiative rate (<i>k</i><sub>nr</sub>) exhibits a straightforward Arrhenius-like relation with the associated <i>E</i><sub>a</sub>. More interestingly, the S<sub>1</sub> → S<sub>0</sub> mechanism proceeds via a highly distorted intermediate structure in which the core BODIPY plane and the substituent at the 1-position are forced to bend, while the internal rotation of the very same substituent induces the <i>La </i>→<i> Lb</i> transition. Our model reproduces <i>k</i><sub>fl</sub>, <i>k</i><sub>nr</sub>, and <i>ϕ</i><sub>fl</sub> to mean absolute errors (MAE) of 0.16 decades, 0.87 decades, and 0.26, when all outliers are considered. These results allow us to validate the predictive power of the proposed data-driven algorithm in <i>ϕ</i><sub>fl</sub>. They also indicate that the model has a great potential to facilitate and accelerate the machine learning aided design of BODIPY dyes for imaging and sensing applications, given sufficient experimental data and appropriate molecular descriptors.</div>