Healthcare domains deal with massive amounts of sensitive data, such as patient health records, diagnostic results, and clinical notes, which must be secured under privacy regulations (like HIPAA). Traditional security technologies cover many problems but struggle against more advanced iterative threats. Implementing deep learning algorithms has resulted in the creation of texts of advanced encryption techniques, which can discover obvious patterns and improve the security of health systems. When combined with Natural Language Processing (NLP), these algorithms can also anonymize and de-identify patient information, allowing healthcare providers to share and collaborate on data without violating patient confidentiality. This will prove valuable for further medical research and improving the quality of telemedicine, where better information transfer is key for better treatment results. Key Approaches: Data encryption, differential privacy, tokenization, and access control are all essential methods of protecting healthcare data, and NLP plays a vital role in ensuring that sensitive health information is handled securely.