In order to explore robust and universal ML-based prediction models, we constructed a new dataset containing the experimental Tg data of 1944 small organic molecules and the experimental Td data of 1182 small organic OLED compounds collected from a large amount of literature. Based on the new dataset, we utilized an ensemble learning approach LightGBM algorithm, rather than the single machine learning methods used in previous works, to integrate multiple weak learners to build an entire learner with better prediction performance than that from any of its component. The prediction performance of our models was verified by two types of out-of-sample datasets, exhibiting satisfactory results and confirming the generality of the models. In addition, experimental validation further confirmed the reliability of our prediction models and potential in practical application. More importantly, we built a website including the optimal Tg and Td prediction models coupled with the new dataset, which is freely available at . We expect that this website will serve as a useful tool to help experimental investigators quickly and accurately estimate Tg and Td.