This study explores integrating quantum computing and machine learning to enhance the accuracy and speed of diagnosing serious diseases, such as Alzheimer’s. It addresses the limitations of current medical prediction systems by examining how these technologies can analyze large medical datasets to identify complex patterns and improve health outcome predictions. The research aims to expand the theoretical understanding of the synergy between quantum computing and machine learning, offering new insights into their medical applications. Methodologically, it presents innovative data analysis techniques and advanced algorithms, leveraging quantum computing’s capacity to process vast datasets and accelerate calculations. In practical terms, integrating these technologies promises to revolutionize medical prediction systems, particularly for early diagnosis, potentially leading to more effective treatments and improved patient outcomes. The project includes a thorough literature review, identifying achievements and gaps in current research. It proposes theoretical models illustrating how quantum computing and machine learning could collaborate to advance medical diagnostics. The research plan is outlined in phases, covering data collection, analysis, and validation of the proposed models. The timeline and budget highlight the resources required, emphasizing the importance of publications and translations to disseminate the knowledge generated. This study aspires to offer transformative solutions by integrating advanced technologies to improve medical practice and enhance patients’ quality of life.