Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine-learning models are a time-intensive process. In this study led by Ather Abbas, PhD student at UNIST, a python-based framework was developed to simplify the process of building and training machine-learning-based hydrological models and to automatize pre-processing of hydrological data and post-processing of model results. This framework, partly tested on M-TROPICS data collected in Lao PDR, will help increase the application of machine-learning-based modeling approaches in hydrological sciences.