In the past few years, the use of hyperspectral imaging for medicinal purposes has increased. Hyperspectral imaging is a trending topic in the remote sensing field. It is used to collect the spectral information present in the scene. Additionally, the combination of spectral and spatial data offers valuable data for classifying brain tumours. When combined with Machine Learning (ML) algorithms, HSI (Hyperspectral Imaging) can be utilised as a non-intrusive medical diagnosis tool. By using hyperspectral images in the medical field, we can classify cancers, tissues, blood vessels etc. This paper offers a gradient boosting based ensembled classification (MCGC) scheme for In-Vivo brain cancer classification. And also, a multi-scale CNN method feature extraction and graph-based clustering method for feature selection are applied to get accurate classification results. The dataset used for brain cancer classification is the In-Vivo brain cancer dataset. These dataset images are captured when the real-time brain tumour surgery was going on. Finally, we performed the experiment results of gradient boosting ensembled classification methods. Support vector machine (SVM) and Random Forest (RF) classification methods were used to compare the outcomes of classification. And in comparison, with the other existing methods, we got good outcomes.