Computers can now learn autonomously due to advancements in machine learning, a technology widely applied across numerous global industries. This field focuses on developing computer algorithms that enhance data usage through experience. Essentially, machine learning involves enabling computers to acquire knowledge from data. It tackles numerous real-world issues, generating excitement in the medical sector. Despite the thousands of published papers on applying machine learning algorithms to medical data, only a few are truly beneficial for medical treatment. Therefore, one of our goals is to identify potential challenges in this area. Understanding the Potential and Limitations of Machine Learning in Healthcare: The promise of machine learning in healthcare has garnered significant attention globally, highlighting its potential for transformative applications. However, only a small fraction of the numerous studies showcasing the effectiveness of machine learning algorithms on medical data have translated into actual medical therapies. This gap underscores several challenges, including concerns about data quality and privacy, the complexity of understanding intricate models, legal and ethical considerations, integration into clinical workflows, and the need for collaboration between technological and medical professionals. Addressing these issues requires multidisciplinary efforts to ensure the responsible and effective adoption of machine learning, ultimately enabling it to truly revolutionize healthcare practices. Several areas of clinical research in machine learning are: 1. Reconstructing diseases 2. Hypothesis testing 3. Recruiting patients 4. Big data 5. Developing diagnostics 6. Improving prognostics 7. Patient monitoring 8. Requiring collaborations