Autism is a neuro developmental condition that affects how a person perceives and interacts with the world with a challenging social communication and interaction, as well as restricted and repetitive behaviors. It is called Autism “spectrum” disorder (ASD) because it affects individuals differently and to varying degrees. Some people with autism may have mild symptoms, while others may have more severe challenges. Individuals with autism may have difficulty in understanding social cues, maintaining eye contact, and engaging in reciprocal conversations and also may have difficulty with verbal and nonverbal communication. To diagnose ASD, researchers have worked with Electroencephalography (EEG), Magnetic Resonance Imaging (MRI), and genetic and sociodemographic data. In this work we have reviewed the existing literature based on EEG for the diagnosis of ASD. EEG is a non-invasive technique used to record electrical activity in the brain. Small metal electrodes are placed on the scalp, and they detect electrical impulses generated by the firing of neurons in the brain. These impulses are then amplified and recorded, producing a visual representation of brain activity known as an EEG. It is commonly used in clinical settings to diagnose neurological disorders such as epilepsy, sleep disorders, and brain injuries. It's also used in research to study brain function. Here we have reviewed mainly EEG-based machine learning approaches to detect Autism. Future directions of research related to Autism are also mentioned.