The incorporation of Fuzzy Artificial Intelligence (AI) in assessing desirability within Healthcare Internet of Things (IoT) environments marks a significant progression in healthcare technology. This investigation aims to elucidate the application of Fuzzy AI as an advanced method for systematically evaluating relative desirability in the complex framework of IoT-based healthcare systems. In the contemporary healthcare landscape, the introduction of IoT has ushered in unprecedented connectivity and data exchange. Effectively utilizing the vast amount of data generated in IoT-enabled healthcare systems requires sophisticated methodologies for discerning and prioritizing the relative desirability of diverse parameters. Fuzzy AI, with its capability to handle imprecise and uncertain information, emerges as a compelling solution for addressing the nuanced complexities inherent in healthcare IoT environments. The underlying premise of this research is grounded in the belief that Fuzzy AI techniques, known for their capacity to model and process vague and uncertain information, can significantly enhance the assessment of desirability in healthcare scenarios. By leveraging fuzzy logic and membership functions, the model aims to provide a nuanced and context-aware evaluation of diverse factors influencing desirability within the IoT-based healthcare ecosystem. Furthermore, this study seeks to contribute to the existing body of knowledge by providing empirical evidence of the efficacy of Fuzzy AI in navigating the intricacies of healthcare IoT environments. Through comprehensive experimentation and analysis, the research endeavors to demonstrate the advantages and potential drawbacks associated with the application of Fuzzy AI in this specific context. The exploration of Fuzzy Artificial Intelligence for assessing relative desirability in Healthcare IoT Environments represents a critical endeavor in advancing the understanding of how cutting-edge technologies can be harnessed to optimize healthcare systems. Through a meticulous examination of Fuzzy AI's capabilities and implications, this research aims to provide valuable insights that may inform the ongoing development and refinement of IoT-driven healthcare solutions. The Preference Leveled Evaluation Functions Method, when employed in fuzzy Artificial Neural Network systems, serves as a robust approach to evaluating the relative desirability of cells in a dataset. The objective of this technique is to create a system capable of accurately identifying abnormalities in cell data and alerting patients accordingly. The methodology involves defining factors and preference levels, aiding in informed decision-making based on multiple factors with varying degrees of importance. Factors crucial for evaluating the smart system dataset, such as rhythm annotations of different types (AFIB—atrial fibrillation, AFL—atrial flutter, J—AV junctional rhythm, N—used to indicate all other rhythms), are identified. For each factor, preference levels, including “Highly Desirable,” “Desirable,” “Neutral,” “Less Desirable,” and “Undesirable,” are defined. Membership functions for each factor and preference level, defining the fuzzy boundaries, are created. Subsequently, a Fuzzy Inference Engine implements fuzzy rules to compute fuzzy output membership degrees for each preference level. The defuzzification process converts aggregated fuzzy preference levels back into crisp values for each preference level using a defuzzification method. This comprehensive approach ensures a nuanced evaluation of cell data, contributing to effective decision-making in healthcare applications.