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Brain tumor remain one of the most life-threatening forms of cancer, and early and accurate diagnosis is crucialfor effective treatment planning and improving patient outcomes. Magnetic Resonance Imaging (MRI) serves as a primary modality for brain tumor detection; however, manual interpretation of these scans is often time-consuming and subject to inter-observer variability. Recent advances in Machine Learning (ML) and Deep Learning (DL) offer promising tools to automate and enhance tumor detection and segmentation in medical images. This research paper presents a comprehensive study on the application of ML and DL techniques for brain tumor detection, focusing on both classification and segmentation tasks. Various algorithms, including traditional ML classifiers and state-of- the-art Convolutional Neural Networks (CNNs), were evaluated on publicly available datasets. The proposed deep learning models demonstrated superior performance in identifying tumor regions with high accuracy and robustness. Furthermore, the paper discusses the challenges associated with data preprocessing, model interpretability, and real- time deployment, particularly in the context of medical science. The results underscore the potential of integrating ML/DL-based systems into clinical workflows to support radiologists and enhance diagnostic efficiency.

This study presents a critical analysis of Health Data Science and its evolving role in transforming healthcare delivery through advanced computational methodologies. By systematically integrating machine learning (ML), big data analytics, the Internet of Things (IoT), and extended reality (XR), the research demonstrates how these technologies contribute to early diagnosis, personalized treatment, and clinical decision support across diverse medical domains such as oncology, cardiology, diabetes care, radiology, and public health. The manuscript examines specific applications including predictive modeling for disease progression, federated learning for privacy-preserving data sharing, and multimodal image fusion using deep neural networks. It evaluates model performance using key metrics such as AUC-ROC and F1-score, highlighting both improvements over traditional diagnostic methods and current limitations in generalizability and real-world deployment. Ethical, legal, and data governance challenges are also discussed, with recommendations for enhancing transparency, fairness, and interoperability in health data systems. Through interdisciplinary collaboration and rigorous data practices, Health Data Science has the potential to foster more responsive, equitable, and patient-centered healthcare solutions. This work contributes actionable insights for clinicians, developers, and policymakers striving to leverage data-driven innovations in clinical environments