The aim of the study, Organizations face increasing difficulties in navigating dynamic global markets, innovative technology, and changing customer habits in an era characterized by volatility, uncertainty, complexity, and ambiguity (VUCA). Traditional decision support systems (DSS), though valuable in structured and stable environments, are increasingly inadequate for handling the scale, speed, and diversity of modern business data. This study explores the transformative role of Deep Learning–Enabled Decision Support Systems (DL-DSS) in strategic business management. By leveraging multi-layered neural networks, DL-DSS can autonomously extract patterns from heterogeneous datasets, ranging from financial records and supply chain logs to customer feedback and social media streams. Based on the data lifecycle, the paper proposes the concept of joint learning algorithm, which consists of three mutually-reinforcing stages of data acquisition and integration, deep learning processing, and decision support layers, which are interconnected through a feedback loop providing continuous learning and adaptation. Empirical simulation in six domains - sales forecasting, sentiment analysis, brand monitoring, supply chain optimization, scenario simulation and prescriptive strategy formulation - reveals the effectiveness of DL-DSS relative to traditional models. Results show measurable and meaningful improvements in predictive accuracy, operational efficiency, and strategic agility, including better sales accuracy forecasting, improved customer sentiment trends, and optimised resource allocation. We highlight the promise of DL-DSS not as a diagnosis tool, but as a proactive strategic partner which can provide prescriptive recommendations in the light of organization goals. Finally, this research validates that the integration of deep learning within DVIS combines to create data-driven capability for building resilience, sustainable competitive advantage, and value creation in rapidly changing business environments.

Data Transmission Unit and Web server Interaction to Monitor Water Distribution: A Cyber-Physical System Perspective