Glioma　is　the　most　aggressive　and　dangerous　primary　brain　tumor　with　a　survival　time　of　less　than　14　months.　Segmentation　of　tumors　is　a　necessary　task　in　the　image　processing　of　the　gliomas　and　is　important　for　its　timely　diagnosis　and　starting　a　treatment.　Using　3D　U-net　architecture　to　perform　semantic　segmentation　on　brain　tumor　dataset　is　at　the　core　of　deep　learning.　In　this　paper,　we　present　a　unique　cloud-based　3D　U-Net　method　to　perform　brain　tumor　segmentation　using　BRATS　dataset.　The　system　was　effectively　trained　by　using　Adam　optimization　solver　by　utilizing　multiple　hyper　parameters.　We　got　an　average　dice　score　of　95%　which　makes　our　method　the　first　cloud-based　method　to　achieve　maximum　accuracy.　The　dice　score　is　calculated　by　using　Sorensen-Dice　similarity　coefficient.　We　also　performed　an　extensive　literature　review　of　the　brain　tumor　segmentation　methods　implemented　in　the　last　five　years　to　get　a　state-of-the-art　picture　of　well-known　methodologies　with　a　higher　dice　score.　In　comparison　to　the　already　implemented　architectures,　our　method　ranks　on　top　in　terms　of　accuracy　in　using　a　cloud-based　3D　U-Net　framework　for　glioma　segmentation.