## 1 Introduction
Our article addresses the below main domains: the functional criteria and operational implications of ML models, the issues confronted in real-world implementations, and the gaps in recent ISs that require more investigation. Additionally, it presents a new taxonomy to categorize ML-powered smart ISs into six distinct areas, increasing the knowledge of their roles and efficiency. Our study also recognizes emerging challenges, models, and future research directions that are important to progress the area, therefore associating with the improvement of more robust and adaptable ML-powered smart ISs. The contributions of this article are:  
* **In-detail evaluation of current issues in ML for multi-objective healthcare management:** This study proposes a comprehensive exploration of the existing issues provided by ML-based ISs in healthcare management. By concentrating on real-world challenges like data interoperability, model interpretability, and adaptability to transforming patient demands, our article presents a nuanced knowledge of how these challenges affect the productivity of ML-based ISs used for multi-objective healthcare management. The evaluation emphasizes significant challenges like the difficulty of incorporating various data sources and ensuring real-time responsiveness, presenting visions into the drawbacks of present ML methods, and their functional implications. * **In-depth exploration of smart ISs used for multi-objective healthcare management:** Our study proposes a thorough survey of different smart ISs that apply ML for addressing several medical purposes. It categorizes these models into six initial fields. For each area, our article dwells on the special ML-powered smart IS applied, defining their functional strategies and how they are associated with increasing medical decision-making. This exploration aids in comprehending the domain and application of ML within various aspects of healthcare;
* **Assessment of customized ML-powered IS applications:** A comprehensive analysis is presented for customized IS applications, containing an evaluation of their accuracy, precision, F1-score, sensitivity, specificity, computational complexity, AUC-ROC, and the datasets and simulation environments applied. By comparing different applications, our study elucidates how customization impacts the effectiveness and productivity of ML-driven ISs and presents a thorough exploration of the data and simulation settings that affect their function;
* **Recognition and examination of significant IS and emerging challenges:** Our study recognizes and explains the important ISs that are critical in recent investigations. It examines the emerging challenges that are relevant to these models, like transforming healthcare demands, technological progress, and the ethical considerations of arranging ML in medical environments. This contribution contains an explanation of the present patterns and innovations in healthcare management, stressing fields where more research is required to address unresolved issues and increase the implementation of ML models in healthcare. The further sections of this paper are structured as follows. Section 2 investigates the fundamental landscapes and terminologies of HISs and multi-objective healthcare management. Section 3 examines related review papers. Section 4 maps out the research process and paper selection techniques. Section 5 selects the chosen papers for inquiry and evaluation and proposes a comparison and explanation. And lastly, Section 6 reveals open issues and examines implications, containing ethical considerations.