In the quickly evolving sector of healthcare, medical data is a useful instrument that requires robust protection to guarantee privacy and legal standard compliance. Maintaining private patient data secure has become rather crucial as artificial intelligence (AI) is used more and more in medical operations like diagnosis, treatment planning, and patient monitoring. This paper examines how fast and accurate medical data can remain safe using secure artificial intelligence systems. We discuss many AI-based encryption, identification, and data anonym sing techniques aimed to reduce risks like data breaches, illegal access, and private medical data use. The report also investigates how decentralized data management that guarantees security and transparency helps blockchain technologies assist and safeguard AI-powered healthcare systems. Developing flexible artificial intelligence algorithms and end-to-end encryption techniques capable of spotting unusual activity possibly compromising data security requires a lot of effort. We also examine the use of federated learning, a method wherein artificial intelligence models may be trained across distributed decentralized data sources without revealing private data. This increases privacy quality and reduces data breaches risk. With specifics on the laws safeguarding patient privacy and data privacy, the paper also addresses the moral and legal consequences of AI-powered data security in the healthcare sector. Our findings highlight the need of striking a balance between innovative AI technology and rigorous security policies to foster confidence and ensure that artificial intelligence is used sensibly and successfully in the healthcare sector.