We focus on the scientific and engineering advances being driven by HPC together with advanced statistical methods featuring artificial intelligence/deep learning/machine learning (AI/DL/ML). Accelerated progress in delivering scientific discovery via predictions with these methods guided by Validation, Verification & Uncertainty Quantification (VVUQ) are enabling data-driven discoveries essential for realizing the grand challenge potential of fusion energy. The best validated fusion devices are tokamaks, which are vulnerable to large scale plasma instabilities called “disruptions” that can stop the reaction and damage the device. Reliably predicting and avoiding these damaging events is essential, and recent advances feature successful capabilities involving the deployment of recurrent and convolutional neural networks in Princeton's Deep Learning Code – FRNN – for carrying out efficient transfer learning across very different tokamak devices. Essential validation vs. a huge data base is documented and highlights the applicability of FRNN to both the large JET and smaller DIIID. It is further shown that this AI/DL capability can provide not only the “disruption score,” as an indicator of the probability of an imminent disruption but also a “sensitivity score” in real-time to indicate the underlying reasons for the predicted event. Consequently, this deep learning capability provides the detailed information for a plasma control system (PCS) that can help improve disruption avoidance in real-time and to thereby optimize plasma performance. Application of such AI/DL methods for real-time prediction and control has been further advanced with the innovative introduction of a surrogate model simulator (SGTC). These capabilities are now leading to exciting avenues for moving from passive prediction to active control and ultimately, to the optimization of the design for a first-of-a-kind fusion pilot plant.