The exponential increase in digital health records, medical imaging data, wearable device outputs, and genomic information has significantly contributed to the rise of big data in healthcare. Big Data Analytics (BDA) offers advanced tools and methodologies to efficiently process, manage, and extract valuable insights from these vast and complex datasets. This paper examines how big data analytics enhances healthcare systems by improving diagnostic accuracy, enabling personalized treatment strategies, supporting predictive modeling, and optimizing hospital operations. Through the integration of machine learning algorithms and data mining techniques, healthcare institutions can uncover hidden patterns, forecast disease outbreaks, and facilitate evidence-based clinical decisions. Moreover, big data supports real-time patient monitoring, which helps reduce medical errors and improve overall health outcomes. Despite its transformative potential, challenges such as data privacy concerns, interoperability issues, and the shortage of skilled data professionals continue to hinder its widespread implementation. Therefore, this study highlights the necessity of adopting secure, scalable, and ethical data analytics frameworks to advance healthcare toward a more proactive, data-driven, and patient-centered model.