## 1 Introduction
By leveraging ML-driven techniques, healthcare structures can evaluate how various decisions affect each purpose, allowing for a more refined comprehension of the compromises and synergies between evolving objectives [17]. This technology promotes real-time data processing and dynamic arrangements to progressive patient demands and functional situations, ensuring that decisions are both data-informed and adaptable [18]. Lastly, ML-driven ISs aid healthcare organizations in the attainment of a balanced technique that aligns with various priorities and limitations, increasing the efficiency and responsiveness of healthcare services while solving the complicated needs of modern healthcare platforms [19]. Despite critical development in smart ISs and their potential for multi-objective healthcare management, there is a considerable lack of broad analysis of these models. This investigation addresses this gap by performing an SLR to thoroughly evaluate the machine-learning (ML) methods utilized for managing various healthcare objectives. The SLR methodology contains a detailed survey of the present study in this area. The evaluation results in a comprehensive exploration of ML-powered smart IS methodologies within healthcare management. In addition, the dependability of the research selection procedure is widely assessed. Further parts present a comprehensive elaboration of the research methodology, containing the special selection metric and transparently determined research questions. The research objectives are:  
1. to systematically detect and classify multi-objective healthcare management that applies ML-driven smart IS, concentrating particularly on the models that optimize patient results, resource allocation, and functional effectiveness through healthcare platforms;
2. to meticulously assess the special limitations and drawbacks related to the incorporation and experimental implementation of ML models in this healthcare management, containing challenges associated with data interoperability, model interpretability, and real-time adaptability;
3. to improve and confirm a new taxonomy that divides smart IS into six distinct fields, namely diagnostic systems, treatment-planning systems, patient monitoring systems, resource allocation systems, preventive healthcare systems, and hybrid techniques, according to their particular applications and efficiency in increasing healthcare service. The research questions are:
4. what are the main applications of ML in multi-objective healthcare management, and how could these models be meticulously categorized according to their operational roles in healthcare, like diagnostics, treatment planning, and patient monitoring;
5. what are the particular issues and obstacles that arise during the implementation and incorporation of ML models in healthcare management, especially regarding data management, model accuracy, and the capacity to answer to dynamic patient demands;
6. how could a novel present taxonomy of smart ISs, classified into diagnostic systems, treatment-planning systems, patient monitoring systems, resource allocation systems, preventive healthcare systems, and hybrid strategies, increase our knowledge of their functional roles and efficiency in developing healthcare results? This examination dwells into the successful application of ML methods across multi-objective healthcare management, presenting an in-detail analysis of these models. Particularly, our study explores the effectiveness of different ML algorithms, reflecting their advantages, drawbacks, and the simulation settings and datasets applied in their advancement. We analyze each paper in terms of accuracy, precision, F1 Score, sensitivity, specificity, computational complexity, AUC-ROC, simulation, dataset, and size of the dataset. By concentrating on an in-detail evaluation of these facets, our paper uncovers how various ML techniques could be efficiently used for clinical and health prediction models.