A vital tool for healthcare analytics, time series analysis provides insightful information on patient outcomes, resource use, and cost control. Time series approaches have the potential to improve policyholder services, maximize claims management, and increase prediction accuracy in the field of health insurance. The use of time series analysis in healthcare analytics is examined in this article, with an emphasis on how it integrates with health insurance. It gives a general review of important methods such as Long Short-Term Memory (LSTM) networks, exponential smoothing, and ARIMA. The paper also addresses the difficulties that time series analysis presents in the healthcare industry, such as the complexity and quality of the data, and it indicates potential future paths for both practice and research. Health insurers and healthcare providers may make data-driven choices that improve patient care and operational efficiency by learning about these techniques and their applications.