Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames that services the Convolutional Long-Short Term Memory (ConvLSTM) model. The input video is segmented into frames, fed to the ConvLSTM model to extract the features and forecast a future frame which can be beneficial in a variety of applications. We have used two metrics to measure the quality of the predicted frame: structural similarity index (SSIM) and perceptual distance, which help in understanding the difference between the actual frame and the predicted frame. The UCF101 data set is used for testing and training in the project. It is a data collection of realistic action videos taken from YouTube with 101 action categories for action detection. The ConvLSTM model is trained and tested for 24 categories from this dataset and a future frame is predicted which yields satisfactory results. We obtained SSIM as 0.95 and perceptual similarity as 24.28 for our system. The suggested work’s results are also compared to those of state-of-the-art approaches, which are shown to be superior.