Various real world applications in science and industry are often recorded over time as asynchronous event sequences. These event sequences comprise of the time of occurrence of events. Different applications including such event sequences are crime analysis, earthquake prediction, neural spiking train study, infectious disease prediction etc. A principled framework for modeling asynchronous event sequences is temporal point process. Recent works on neural temporal point process have combined the theoretical foundation of point process with universal approximation ability of neural networks. However, the predictions made by these models are uncertain due to incorrect model inference. Therefore, it is highly desirable to associate uncertainty with the predictions as well. In this paper, we propose a novel model, Ensemble Neural Hawkes Process, which is capable of predicting event occurrence time along with uncertainty, hence improving the generalization capability. We also propose evaluation metric which captures the uncertainty modelling capability for event prediction. The efficacy of proposed model is demonstrated using various simulated and real world datasets. © 2023 ACM.