This paper introduces a new method for predicting and tracking cricket ball trajectories, which is important for analyzing player and team performance. The method uses transfer learning with well-known convolutional neural network (CNN) models like ResNet50 and AlexNet, along with a custom CNN. In the first stage, these models are fine-tuned using a dataset of labeled ball trajectories to learn the complex patterns of cricket ball movements. Then, a combination of object detection and motion estimation techniques is used to track the ball in video frames. The experiments, conducted on diverse cricket scenarios, show that the proposed approach, leveraging pre-trained CNN models, is more accurate than traditional methods, highlighting its potential in sports analytics. In this performance analysis, different classifiers are used for cricket ball trajectory prediction and tracking. Among all these classifiers custom CNN is giving better performance over other state-of-the-art classifiers.