Highly relevant to SCO prediction, ML models have been trained to predict more challenging properties such as the spin states of transition metal complexes (TMCs). Prior studies from our group used different ML approaches to predict SCO behavior. Through a combination of genetic algorithm and artificial neural networks (ANNs) trained on density functionals with different amounts of HF exchange 102 , SCO behavior was predicted through evaluations of ML-predicted 102 spin splitting energies. Another study or neural networks trained using only the local structure around the TM centers. While ML has been previously leveraged to predict SCO behavior through predictions of spin splitting energies or structural differences between spin states, in this study we use ML models to make predictions of transition temperatures of SCO complexes from the previously curated SCO-95 data set. We train interpretable ML models, i.e., random forest (RF) on a full feature set as well as RF or kernel ridge regression (KRR) on a feature sets selected by RF-ranked recursive feature addition (RF-RFA), on a set of 76 SCO complexes for which experimental T1/2 values are available, and we show that these models perform as well or better than the previously-identified best-performing DFAs at predicting experimental transition temperatures. We perform feature selection to identify the most important properties for predicting T1/2 values, showing that nonlocal features are essential to distinguishing the behavior of known SCOs. We also make predictions of T1/2 values for a set of 18 SCO complexes for which experimental T1/2 values are not reported but can be inferred based on experimental conditions, and, despite the small training set size, we show ML models to outperform the best-performing DFT functionals on this task.