Predictive Early Stopping is a cutting-edge approach designed to enhance the efficiency of model training and hyperparameter optimization in machine learning by predicting when to halt training of underperforming models. Built upon insights from existing projects like Learning Curve Extrapolation and Hyperband, it utilizes data from millions of models on the Comet platform to generalize predictions across hyperparameters and architectures. Benchmark studies demonstrate that Predictive Early Stopping can accelerate training by up to 30%, allowing significant reductions in computational time and cost. Testing results show that it effectively reduces the number of epochs needed for hyperparameter sweeps, thereby freeing up resources and promoting the environmentally friendly concept of "Green AI." As an add-on for Comet Teams or Enterprise, it aims to lower financial barriers in AI research by minimizing wasted computational efforts, aligning with the Allen Institute for AI's call for resource-efficient AI development.