Next-Generation Decision Support Systems: Integrating Artificial Intelligence, Machine Learning, Big Data Analytics, and Intelligent Information Systems

Decision support systems are moving from model- and data-driven systems to integrated intelligent environments where complex, dynamic and information-rich decisions can be supported. This integrative review examines the combined contribution of artificial intelligence, machine learning, big data analytics, and intelligent information systems to the development of next-generation decision support systems. The literature reviewed indicates that AI and machine learning can enhance prediction, reasoning, automation and adaptive learning, and big data analytics can facilitate the processing of large-scale, real-time and heterogeneous data. Information systems with intelligence play a role in the organization, contextualization and integration of knowledge in the operational and strategic decision-making process. The review also emphasizes the need to integrate all these features in a single architecture for connecting data acquisition, analytics, intelligent modelling, contextual information, user interaction, and human oversight. Advanced DSS has strategic applications in sectors such as healthcare, finance, manufacturing, supply chains, transportation, and agriculture, to name a few. Significant difficulties in explainability, data quality, interoperability, scalability, trust and organizational readiness persist. Future research should focus on more integrated, human-centered, and adaptive DSS, using more robust evaluation frameworks, and more standardized and cross-domain validation. The general trend of next-generation DSS technologies should be a focus on not only computational abilities but also clarity, relevance, and decision value

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