With the advancement of technology and ever-growing trough of data around the world there has been a fast increase in the accessibility of clinical databases and clinical symbolisms to everyone around the globe. The inaccuracy of the world in foreseeing oncoming and identifying present diseases before they worsen, from these data troughs has invited the network of researchers to soak up the challenges in this area. The crucial apprehensive system of homo sapiens is specially composed of the combination of the cerebrum, pons, cerebellum, encephalon which we in short call as the ‘Brain’. This brain is then connected to the rest of the body via the Spinal cord. The human mind tosses and throws a wide variety of demanding situations and a great number of difficulties to the community of researchers. Machine Learning(ML) capabilities furnish machines with the capacity to become competent in domains without being clear-cut coded for the same. Data from various clinical sources was dissected adequately utilizing ML calculations and interpretations were made on the outcomes. Vital elements of the chosen research— wings of the healthcare system, mining of data, various kinds of analytics, information, data and statistical assets— were extricated to give a methodical perspective on advancements in this field and conceivable future bearings. Absence of prescriptive investigation in exercise and integration of domain professionals in the dynamic procedure, stresses the need of examination and research in the upcoming future. In this paper we survey the various methods used for the implementation of ML backed technology into serving the needs of the medical field in both. Detection classification of a brain tumour from an MRI.