  The demand for novel detector mediums such as Water-based Liquid Scintillator
(WbLS) has increased over the last few decades due to their capability for both
low energy particle interactions and higher light yield. Recently, the usage of
machine learning (ML) methods in high-energy physics has also been increasing.
The ML and AI methods are used in many physics projects in the field since they
provide effective and sensitive results. In this study, we aimed to develop a
comprehensive analysis of water Cherenkov detectors and perform physics
analyses to efficiently separate Cherenkov and scintillation photons with ML
algorithms using the data from the WbLS detector environment. The main goal of
this study was to produce more precise solutions to physics problems, such as
signal classification, by applying ML techniques to the simulation and
experimental data. Here, we trained more than 20 ML models, and our results
revealed that three machine learning models, XGBoost, Light GBM, and Random
Forest models, and their ensemble model gave us more than 95\% accuracy for
separating Cherenkov and scintillation photons with balanced and unbalanced
datasets. This is a significant increase in efficiency as compared with the
results of the classical method by applying simple time cuts.
