Advancing decision-making: A comprehensive review of intelligent systems, applications, and challenges

Abstract

The rapid evolution of intelligent systems, powered by artificial intelligence and machine learning, has created a fragmented research landscape. While numerous studies exist on specific applications, a holistic synthesis of their architectures, taxonomies, applications, and challenges is absent. This paper will bridge this gap by providing a comprehensive systematic review that integrates these disparate elements. This paper conducts a systematic review of over 100 peer-reviewed scientific publications, following a structured process to identify, analyze, and synthesize the current state of intelligent systems research. The review encompasses a wide range of domains, including healthcare, cybersecurity, data mining, and industrial automation. Our analysis yields a unified taxonomy and clarifies the core architectural components of intelligent systems. We identify and categorize key application domains and demonstrate their transformative impact. The review also synthesizes prevailing challenges, such as data quality, scalability, and ethical concerns, and pinpoints emerging trends, including the rise of multimodal AI and hybrid intelligent systems. To the best of our knowledge, this is the first review to offer a consolidated framework that integrates the architecture, taxonomy, applications, and cross-domain challenges of intelligent systems into a single reference. This work serves as a foundational guide for researchers and practitioners, facilitating future advancements in the development of efficient, scalable, and context-aware intelligent systems.