Predictive Early Stopping is a cutting-edge technique aimed at enhancing the efficiency of model training and hyperparameter optimization in machine learning by estimating the convergence value of loss curves, thus enabling the termination of underperforming models early. This method, underpinned by insights from Learning Curve Extrapolation, Hyperband, and Median Stopping, leverages data from over two million models on the Comet platform to generate predictions that are applicable across various hyperparameters and model architectures. Benchmarking studies reveal that it can speed up model training by up to 30%, offering significant time, energy, and cost savings by reducing unnecessary compute cycles, which also aligns with the emerging Green AI movement that emphasizes computational efficiency over sheer accuracy. Experiments with CNN models and Hyperband configurations demonstrate Predictive Early Stopping's ability to achieve comparable outcomes with considerably fewer resources, supporting a shift towards more environmentally friendly AI research practices. This tool is positioned to lower monetary barriers in AI research and is available as an add-on with Comet Teams or Comet Enterprise, with patent applications pending.