Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices. Author Summary We proposed an energy landscape method to quantify the occurrence probability of network states in magnetoencephalograph (MEG) oscillatory power during rest, which was derived from a pairwise maximum entropy model (pMEM). We compared the energy landscapes measures of three resting-state networks between patients with juvenile myoclonic epilepsy (JME) and healthy controls. The pMEM provided a good fit to the binarized MEG oscillatory power in both patients and controls. Patients with JME exhibited fewer local minima of the energy and elevated energy values than controls, predominately in the fronto-parietal network across multiple frequency bands. Furthermore, multivariate features constructed from energy landscapes allowed significant single-patient classification. Our results further highlighted the pMEM as a descriptive, generative, and predictive model for characterizing atypical functional network properties in brain disorders.