As the need for precise, continuous, and non-invasive health monitoring increases, multi-sensor data fusion has become a critical component of contemporary healthcare systems. This chapter examines the development and present state of data fusion frameworks, providing an in-depth overview of the terminology, classification systems, and algorithmic approaches that define the field. Beginning with foundational concepts and key classifications, the chapter examines the most widely adopted fusion technique, including Kalman filters, Bayesian inference models, and machine learning approaches, and assesses their roles in enhancing the reliability and precision of health monitoring systems. Drawing from fusion applications in defense, autonomous driving, robotics, and image analysis, the discussion contextualizes how advancements in data fusion have informed healthcare solutions. A particular focus is placed on biomedical applications such as heartbeat detection, respiration rate estimation, and the detection of sleep apnea, arrhythmias, and atrial fibrillation. The chapter also addresses critical challenges, such as data heterogeneity and sensor reliability, which emphasizes the need for intelligent fusion algorithms. Ultimately, this chapter provides both a technical foundation and a forward-looking perspective on the role of multi-sensor data fusion in healthcare.