Impact Factor(JCC): 6.4687 – This article can be downloaded from www.impactjournals.us IMPACT: International Journal of Research in Engineering and Technology ISSN (P): 2347–4599; ISSN (E): 2321–8843 Vol. 12, Issue 4, Apr 2024, 1–12 © Impact Journals ADVANCED DATA MINING TECHNIQUES IN HEALTHCARE: BRIDGING AI AND BIG DATA Naresh Kumar Reddy Panga Virtusa Corporation, New York, USA Received: 16 Apr 2024 Accepted: 19 Apr 2024 Published: 24 Apr 2024 ABSTRACT The incorporation of big data analytics and artificial intelligence (AI) into mobile health (m-health) systems represents a significant breakthrough in contemporary healthcare. This project aims to research on how wearable sensors, IoT devices and mobile devices can be leveraged for improvising the m-health systems using AI & Big Data. Data preparation before analysis consists of cleaning, transformation and combining data. Health care specialists can leverage the power of big data technologies as Hadoop, and Spark or with contemporary machine learning algorithms to assist them in better decision making based on useful for different purposes information. The paper also provides a blueprint for the ideal way to embed big data and AI in m-health approach focused on apps, strategies and sources of knowledge so as better patient care while getting most out scarce resources. KEYWORDS: Mobile Health, m-Health, Artificial Intelligence, Big Data Analytics, Data Cleaning, Data Integration INTRODUCTION Mobilhealth (Mobile health) or m-health is a term used for the practice of medicine and public health supported by mobile devices. This is a significant advancement in modern technology. In recent times m-health contains big data analytics and artificial intelligence (AI) to construct efficient health care systems. It comes in a myriad, unordered and often very sophisticated kinds of datas - from the advanced language, medical photographs to electronic health records (EHRs) Some of this unstructured data has been introduced through the expansion is mobile apps and healthcare systems. This research investigates in which way AI and big data analytics can be used to improve the m-health systems. It includes a wide range of AI-based algorithms and big data frameworks, focusing on the sources of data, as well as methods in different application areas. The paper also discusses how AI and big data analytics can provide actionable insight to help users plan more efficiently, the allocation of resources specifically depending on areailing m-health concerns. A big data analytics and AI based model for m-health is proposed using it. To better manage m-health data, the outcomes will guide future development of strategies that combine AI and big dataprocessing. Use of mobile-based technology for medical and public health objectives, including personal digital assistants (PDAs), mobile phones, and other wireless gadgets, is known as mobile health. SMS and voice calling capabilities are frequently used in this process on mobile phones. Over 500 m-health projects and close to 40,000 mobile applications with a medical focus are active globally at the moment. Mobile medical devices are capable of monitoring a wide range of health parameters, including blood pressure, heart rate, blood sugar, sleep patterns, and brain activity. Advanced technologies including Bluetooth, GPS, General Packet Radio Service (GPRS), 3G and 4G mobile networks, and more are