Title: Advances in Brain Tumor Detection and Localization: A Comprehensive Survey A paper recently published as a book chapter in a book "INTELLIGENT DATA ANALYTICS FOR BIOINFORMATICS AND BIOMEDICAL SYSTEMS" by Scrivener and Wiley publishing. This survey highlights the evolving landscape of brain tumor detection and localization, highlighting the power of deep learning in improving diagnostics and treatment planning. Various algorithms, such as K-means and convolutional neural networks (CNNs), enhance the detection and localization accuracy. Support vector machine (SVM) have emerged as effective classifiers. Multi-CNN architectures improve accuracy, while combining algorithms such as K-means and Fuzzy K-means enhance segmentation. Moreover, Convolutional Neural Networks consistently excel in brain tumor detection. Deep learning, particularly CNNs, offers superior performance, although complexities and resource constraints should be considered.