Conventional biomarkers, e.g., spikes and high-gamma oscillations, localize EZ inconsistently, which may contribute to variable postsurgical seizure freedom (30~80%). The novelty of this research entails application of the criticality hypothesis (brains benefit from operating between order and disorder) to new biomarker development for improving EZ-localization. We advanced novel markers for high seizure risk brain areas and validated them in conjunction with connectivity features on simulated and SEEG resting-state data. The features were used to train supervised classifiers to localize the epileptogenic network (EpiNet) identified using ictal recordings by physicians. For epileptogenic mechanism generalization, unsupervised classification was used to identify distinct cohort-level SEEG clusters by feature similarity. Supervised classification revealed that combining criticality and connectivity features yielded better EpiNet-classification than using single features (area under the ROC reaching 0.85 vs 0.6~0.7, respectively), which explains why single markers may localize EZ inconsistently. Unsupervised classification revealed a cohort-level pathological cluster of brain areas that globally engaged in strong synchrony and locally showed high amplitude bistability, inhibition dominance, and aberrant power-law scaling – a striking resemblance to our model in a high seizure risk phase. This pathological-like cluster also contained brain regions that did not engage in seizures that would have been conventionally identified as healthy regions. Our findings suggest that EZ dynamics involve both local and network anomalies, which supports a multi-component hypothesis for EZ. We are currently investigating the pathological signatures revealed here within clinical epilepsy MEG inter-ictal recordings which preliminarily show similar power spectrum abnormalities as clinical SEEG data.