https://journals.smarcons.com/index.php/mls <p>Medical and Life sciences (MLS) is a peer-reviewed, Open Access journal that publishes original research articles, Case studies, letter to editor, and review articles etc. covering a wide range of subjects in medicals and life sciences. <span lang="EN-US">All manuscripts are subject to peer review and are expected to meet standards of academic excellence. If approved by the editor, submissions will be considered by peer reviewers, whose identities will remain anonymous to the authors.</span></p> SMARCONS en-US Medical and Life Sciences 2976-7245 https://journals.smarcons.com/index.php/mls/article/view/475 <p>Resource optimization in healthcare has emerged as a critical strategy to balance rising global healthcare demands with constrained financial, infrastructure, and human resources. This review synthesizes recent advances, challenges, and opportunities in optimizing healthcare delivery through data-driven allocation, scheduling, and workload management. Historical milestones reveal a trajectory from early applications of research in clinical logistics to the integration of explainable Artificial Intelligence (AI) and predictive platforms in modern systems. Foundational pillars of optimization, such as resource allocation, workload balance, and scheduling, are shown to directly impact patient wait times, staff burnout, and cost efficiency. The review highlights the transformative role of AI, Machine learning (ML), and operations research (OR) in predictive modeling, real-time decision support, and dynamic scheduling. Case studies demonstrate the success of AI-powered hospital bed forecasting, ML-driven surgical scheduling, and predictive staffing models in improving patient flow, reducing variability, and enhancing responsiveness during crises. Likewise, the integration of the Internet of Things (IoT), cloud, fog, and edge computing has enabled real-time monitoring and rapid decision-making in critical care. Spatial optimization models, including maximal coverage and P-median approaches, are further examined in their role in ensuring equitable healthcare access, particularly in underserved and rural regions. Despite these achievements, significant challenges persist. Data fragmentation, algorithmic bias, resistance to organizational change, and regulatory complexities hinder large-scale implementation. Solutions proposed include ontology–based interoperability frameworks, Blockchain-enabled data governance, fairness-aware algorithms, and structured change management models. Emerging trends highlight the increasing importance of equity-driven spatial optimization, hybrid AI-GIS approaches, and collaborative governance to reduce disparities and enhance resilience. This review concludes that resource optimization in healthcare is no longer an ethical option but a strategic imperative. By harnessing predictive analytics, advanced optimization algorithms, and equity-centered frameworks, healthcare systems can achieve efficiency, resilience, and fairness. However, realizing these benefits requires a multidisciplinary approach that integrates technological innovation, policy alignment, and stakeholder engagement. The future of healthcare resource management lies in scalable, explainable, and equitable solutions capable of adapting to dynamic global health challenges</p> Waleed Arshad Muneeb Arshad Maheen Arshad Muhammad Asjad Khan Fizza Shahid Sara Aslam Arooba Copyright (c) 2026 2026-08-01 2026-08-01 5 2 1 18 10.69565/mls.v5i2.475