## 1 Introduction  
In this section, we investigate the significance of the application of ML-powered smart information systems in multi-objective healthcare management [1]. The emergence of IS has deeply changed the healthcare domain by integrating professional technologies like machine learning (ML) and complex data analytics [2]. These structures have evolved patient care and functional procedures by facilitating more precise detections, improving highly individualized treatment plans, and enabling proactive healthcare interventions [3]. By controlling the power of extensive and various healthcare datasets, ISs are able to carry out complicated evaluations that detect trends and produce actionable landscapes that are critical for decision-making [4]. This ability not only increases diagnostic accuracy and treatment effectiveness but also reinforces preventive healthcare measures and facilitates frequent, real-time patient tracking [5]. Moreover, ISs play a remarkable part in extracting creativity in the healthcare area, being associated with a transfer toward patient-oriented care models and more effective healthcare services [6]. They allow healthcare professionals to attain functional excellence by streamlining procedures and maximizing resource allocation [7]. In addition, these structures contribute to developing public health findings by supporting a data-driven method to control health patterns and solve evolving issues [8]. Essentially, the incorporation of ISs into healthcare systems matches with more extensive objectives of increasing patient care, gaining functional effectiveness, and promoting a more agile and efficient healthcare environment [9]. Decision-making in healthcare management is a complicated analytical procedure that seeks to solve and revolve numerous, usually contradictory, purposes through a decision-making hypothesis [10]. Contrary to traditional single-objective methods that concentrate on optimizing one particular parameter, multi-objective decision-making consists of assessing multiple metrics simultaneously, like cost, quality, efficiency, risk, and stakeholder contentment, each of which can influence decision-making in various ways [11]. This technique needs a careful balance between these various goals, demanding the application of complex techniques to evaluate and handle the trade-offs among them. The procedure includes designing and evaluating how various options operate across all related standards, usually applying specific devices and methods to control the complications of numerous, evolving objectives [12]. The purpose is to recognize solutions that gain the best comprehensive balance or trade-off between the different goals, instead of optimizing for a single finding [13]. This holistic method is especially precious in complicated scenarios, like in healthcare management, where decisions should align with a broad variety of priorities and limitations. By incorporating numerous goals into the decision-making systems, multi-objective decision-making presents a more comprehensive and refined understanding of possible solutions, allowing for more informed and balanced options that align with wider important objectives and efficiently solve the various demands and interests of stakeholders [14]. Nevertheless, decision-making in healthcare management, particularly while extracted by AI, is a complex technique that uses sophisticated computational approaches to simultaneously solve an area of usually inconsistent goals in the healthcare field [15]. This procedure includes incorporating multiple goals, like raising patient results, optimizing resource allocation, managing expenses, and developing functional effectiveness, into a cohesive decision-making system. AI technologies, consisting of ML, predictive analytics, and data mining, play a pivotal part in this method by assessing broad and complicated datasets to uncover patterns, predict patterns, and present actionable visions [16].