In this paper, the remaining useful life of a turbofan engine is prognosed by using a ConvNet-based deep neural network (DNN). The proposed model is based on ConvNet that uses dilated convolutional neural network (CNN) and the concept of the EfficientNet and builds the model using only the CNN algorithm. Most existing studies predicted the remaining useful life of a turbofan engine by learning sequential data based on CNN-RNN (Recurrent neural network). However, due to the inherent characteristics of the RNN algorithm, it has a disadvantage that the number of parameters increases and the calculation time is particularly longer than that of a CNN-based algorithm. The RNN algorithm has many limitations for quick diagnosis and compact model configuration for actual industry utilization. The experimental results show that the ConvNet-based DNN model is suitable for predicting the remaining useful life of a turbofan engine as the proposed model achieves high accuracy and efficiency while dramatically reducing parameters.