Brain tumor detection through machine learning has gained significant traction due to its potential for early diagnosis, accurate classification, and automated segmentation in clinical settings. The success of such models is closely tied to the quality and availability of annotated datasets. This survey presents a comprehensive review of major publicly available datasets – including BraTS, Figshare Brain MRI, TCGAGBM/LGG, REMBRANDT, CQ500, and IBSR – highlighting their imaging modalities, annotation protocols, dataset sizes, and clinical relevance. Special emphasis is placed on BraTS for segmentation and Figshare for multi-class classification. Genomics-integrated datasets like TCGA and REMBRANDT support multi-modal learning, while IXI and CQ500 are valuable for pretraining and emergency diagnostic models. The survey identifies key limitations, such as inter-observer variability, class imbalance, and inconsistent annotation formats. It also underscores the need for more diverse, standardized, and richly labeled datasets. By evaluating the strengths and weaknesses of existing resources, this work provides guidance for selecting suitable benchmarks and suggests future directions such as federated learning and synthetic data augmentation to improve clinical robustness.