A brain tumor is an abnormal tissue mass resulting from abnormal cell growth. Brain tumors often reduce the length of a person’s life and may cause death in advanced tumor cases. Physician teams rely on early detection and accurate tumor placement by magnetic resonance imaging to assess the tumor's pace and accuracy. Treatment, as well as determining the causes of injury to brain cells, further aids in reducing any potential problems the patient could experience. Segmenting images of brain tumors taken by magnetic resonance imaging is important for neurosurgeons. It is not an easy matter and requires high experience from radiologists. Therefore, there is a need for an expert and intelligent system to segment the abnormal part of the medication that is expert, intelligent and designed to address this task. One of the most promising innovative approaches in the medical industry is artificial intelligence. Automatically identifying the aberrant region of the brain is made possible by the application of artificial intelligence in medical imaging, which is dependent on picture interpretation. The goal of this research is to provide a brief survey on automatic methods for tumor segmentation using artificial intelligence methods, which includes the use of machine learning and deep learning methods, which include several methods, including (CNN, RES NET, MOBILE NET etc) that are applied in medical field, and to identify the most important and most accurate methods to obtain results for the automatic segmentation of brain tumor images.