This book offers an in-depth exploration of the latest advancements in decision-making systems, with a particular focus on their applications in modern business and control environments. This book presents a comprehensive framework for understanding how complex decision-making processes can be optimized through the integration of artificial intelligence (AI), machine learning, big data, and advanced mathematical models. Throughout the book, readers gain insights into a wide range of decision-making techniques, from predictive analytics and optimization algorithms to multi-criteria decision analysis (MCDA) and risk management. Emphasizing practical applications, the book showcases real-world case studies from various industries, including finance, supply chain management, and healthcare, highlighting how businesses can leverage these cutting-edge systems for improved efficiency, profitability, and strategic decision-making. Key topics include: The role of AI and machine learning in automating and enhancing business decisions, Data-driven decision-making frameworks, including big data analytics and real-time decision support, Optimization methods used to solve complex business problems in dynamic environments, Human-machine collaboration and decision support systems (DSS) for effective business operations, Risk, uncertainty, and decision theory in business contexts. With contributions from leading experts in the fields of systems, control, and decision-making, this book serves as an essential resource for researchers, practitioners, and students interested in the intersection of business management, advanced technology, and decision science. Whether you are seeking to understand the theoretical foundations or explore practical applications, this book provides a thorough, accessible, and forward-looking examination of the future of decision-making systems in business.