Human movement prediction is a key machine learning domain whose purpose is to predict future movement from previous motion patterns and context information, with usage in autonomous vehicles, virtual reality, video games, and health care. In this study, the goal is to apply Convolutional Neural Networks (CNNs) for predicting human movement from the UCF50 dataset, whose collection contains action videos with a wide variety of actions. CNNs excel at discovering spatial and temporal patterns from video data and, thus, can be used in understanding motion complexities. In this work, a CNN-based approach is developed using a CNN architecture to assess motion dynamics and make accurate forecasts about future moves. By systematically preprocessing the dataset and optimizing the model’s architecture, the study achieved an accuracy of 99.09 demonstrating the reliability and efficiency of CNNs in motion prediction tasks. Furthermore, the paper discusses existing methodologies in human motion prediction, comparing their performance and highlighting the advantages of CNNbased models in processing visual data. The results here bring out the potential of CNNs for real-world applications and set the foundation for future advancements in human activity recognition. The current study adds insight into machine learning methods and how they can be used to enhance motion prediction, with implications toward innovations in those fields that rely on precise modeling of human activities.