Abstract The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta ( β N ) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance β N efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same β N by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing β N during the suppression phase and sustaining the β N -enhanced suppression state by optimizing the RMP strength. The integrated process achieves β N up to ∼2.65 during the suppression phase, which is ∼10% higher than the previous KSTAR record but ∼6% lower than the target of the K-DEMO first phase ( β N = 2.8), and maintains the suppression phase above the lower limit of target β N (= 2.4) for ∼4 s (∼60 τ E ). In addition to β N enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of β N compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of β N and the electron temperature.