Integrated Methodologies for Intelligent Systems: A Multidisciplinary Review of Agile Processes, Algorithmic Optimization, and Domain-Specific Applications

Abstract

The contemporary technological landscape is defined by the convergence of robust software process management, advanced artificial intelligence (AI) algorithms, and specialized hardware applications. This paper presents a comprehensive systematic review and synthesis of recent developments across these interconnected domains. We begin by analyzing the evolution of software development methodologies, specifically the integration of Scrumban and Agile frameworks in global contexts, noting their critical role in managing complex engineering projects. The scope then widens to examine the implementation of intelligent algorithms in robotics and cloud computing, focusing on path-finding optimization, human-machine interfaces, and resource allocation strategies. Furthermore, the review investigates the practical deployment of machine learning in public sector applications, including healthcare diagnostics, environmental monitoring, and agricultural management. Finally, we address the material foundation of sensing technologies, exploring advancements in fiber optics and biomedical materials. By synthesizing evidence from diverse fields—ranging from cloud caching strategies to autonomous medical robotics—this article argues that the future of intelligent systems relies on a holistic approach that unifies process efficiency, algorithmic precision, and material innovation. The findings suggest that while individual domain advancements are significant, the cross-pollination of Agile management with AI-driven control systems offers the highest potential for operational excellence