Schizophrenia is a debilitating mental disorder with several genetic and environmental risk factors. Accurate diagnosis of schizophrenia is difficult owing to factors like the subjective nature of the diagnosis, the absence of a single pathognomic symptom ora laboratory test. Electroencephalographic microstates are brief and stable brain scalptopographies representative of global neuronal activities and several studies report that certain EEG microstates are selectively affected in individuals with schizophrenia. This systematic review of 22 articles investigated the potential of aberrant EEG microstate patterns to become novel biomarkers of schizophrenia and found out an EEG microstate-based biomarker of schizophrenia is plausible but the diagnostic criteria need to be tailored based on the type and severity of the symptoms, treatment status, age of the patients and stage of the disorder. A novel and comprehensive diagnostic criterion based on machine learning is recommended to efficiently incorporate different microstate parameters and combine them with other subjective and objective criteria to increase accuracy and objectivity.