Edge computing is increasingly used for Artificial Intelligence (AI) purposes to meet latency, privacy, and energy challenges. Convolutional Neural networks (CNN) are more frequently deployed on Edge devices for several applications. However, due to their constrained computing resources and energy budget, Edge devices struggle to meet CNN's latency requirements while maintaining good accuracy. It is, therefore, crucial to choose the CNN with the best accuracy and latency trade-off while respecting hardware constraints. This paper presents and compares five of the widely used Machine Learning (ML) based approaches to predict CNN's inference execution time on Edge GPUs. For these 5 methods, in addition to their prediction accuracy, we also explore the time needed for their training and their hyperparameters' tuning. Finally, we compare times to run the prediction models on different platforms. The use of these methods will highly facilitate design space exploration by quickly providing the best CNN on a target Edge GPU. Experimental results show that XGBoost provides an interesting average prediction error even for unexplored and unseen CNN architectures. Random Forest depicts comparable accuracy but needs more effort and time to be trained. The other 3 approaches (OLS, MLP, and SVR) are less accurate for CNN performance estimation.