Neonatal sepsis is a dangerous non-specific disease in babies, especially neonate/newborns. It is one of the leading causes of neonate mortality rate, because of the difficulty to diagnose, leading to late or false treatment. Previous research has found the promising feature of artificial intelligence or machine learning in solving the problem. After analysing hours of the electronic health record data available, they are able to diagnose sepsis condition on neonates. However, the accuracy and time needed before diagnosis are still concerning considering the risk of mistreated or late diagnosed sepsis cases. In this research, machine learning and thermal imaging technology is used to explore the possibility of predicting sepsis. 57 thermal videos from 26 babies are processed to track the highest skin temperature visible to the thermal camera. The temperature data then is utilized to train and test several machine learning models for predicting sepsis cases. Support Vector Machine (SVM) was found to be the best sepsis predictor using time-series variation of the temperature data as the feature. The model needs 10-30 minutes of thermal recording, 19 minutes in average, to predict sepsis and achieved 82\% accuracy. Simulation also shows the high possibility in increasing the accuracy when more data/thermal videos are available to train the model. High accuracy model with fast reacting sepsis prediction could help doctors precisely treat septic neonates in timely manner, decreasing the mortality rate for sepsis cases.