Blockchain technology (BT) offers a good opportunity to build a decentralized, secure and verifiable system for data modification and authentication across transactions. With a focus on privacy and reliability, BT is just entering the healthcare sector and offers robust data protection. However, modern security issues come with huge threats, and like any evolving technology, double spending occurs. Advanced Encryption Standard (AES) is used for data encryption and addresses these issues using state-of-the-art methods. Furthermore, the integration of machine learning (ML) approaches with encrypted data analytics is proposed to improve the accuracy of the decision-making process. In the context of health data, applying ML techniques such as Naive Bayes to identify fitness-related information further strengthens security mechanisms. This study focuses on the potential synergies between ML and BT and their impact in building powerful and resilient applications in the field of healthcare security, providing a detailed analysis of the use of ML approaches to support the security of blockchain-based healthcare systems.