Artificial Intelligence techniques such as Deep Learning (DL) and Machine Learning (ML) have significantly advanced the medical sector, improving disease diagnosis, patient monitoring, and medical decision-making processes. Among these techniques, Fuzzy Logic stands out due to its ability to handle uncertainty, imprecision, and vagueness—characteristics frequently encountered in diverse medical scenarios. This survey provides a comprehensive review of Fuzzy Logic’s role within the healthcare domain and its integration with other AI techniques, highlighting its transformative impact on both physical and neurological healthcare, Including mental health. The study explores real-world applications, such as cancer, heart disease, and kidney disease detection. In neurological health, it encompasses diagnosing conditions such as Alzheimer’s, depression, anxiety, and autism spectrum disorder, as well as enabling personalized therapy and real-time monitoring. By synthesizing recent advancements, this research demonstrates how Fuzzy Logic improves diagnostic accuracy, reduces errors, and enhances patient outcomes, with some studies achieving up to 99% accuracy in specific applications. FL provides interpretable decision-making, which promotes trust among medical professionals unlike black-box AI models. Finally, the study outlines future research directions, emphasizing the potential of AI-driven fuzzy systems in precision medicine and the development of intelligent, adaptive, and explainable healthcare solutions.