Medical images play an important role in the diagnosis of diseases effectively. Human brain is consisting of millions of neurons which work in proper coordination with one another, and human behavior is an outcome of the response of neurons to internal/external motor or sensory stimuli. These neurons are the carriers of signals from different parts of the human body and the brain. Human cognition studies focus on interpreting either these signals or brain images and there are various technologies which are in use for brain disease studies. Every image or technology generates lot of data in different form which can be modeled using Artificial Intelligence models. The electroencephalogram (EEG) is a recording of the electrical activity of the brain from the scalp. The recorded waveforms reflect the cortical electrical activity. Signal intensity: EEG activity is quite small, measured in microvolts (mV). Delta, Theta, Alpha and Beta are the main frequencies of human EEG waves and EEG electrodes are used to generate EEG plots. In this chapter, authors have tried to capture details on various technologies, techniques, usage of AI models in Brain mapping for disease predictions. This chapter presents a comprehensive account on them and will appeal to researchers in the field of human cognition.