The era of Internet of Things (IoT) technology and Decision Support Systems (DSS) has caused a paradigm shift in how businesses make decisions. This networked ecosystem generates large amounts of real-time data, providing unique insights into a variety of processes and environments. The vast amount and high speed of data generation pose significant challenges in extracting critical insights quickly and accurately. Decision Support Systems (DSS) leverage advanced analytics and algorithms to transform raw data into valuable information, facilitating swift and informed decision-making. By utilizing data streams generated from the Internet of Things (IoT), DSS provides real-time situational awareness, predictive analytics, and prescriptive recommendations. This capability is beneficial across various fields. The decision support modules employ a number of analytical techniques, including statistical models, machine learning algorithms, optimization algorithms, and expert systems. These modules analyze incoming data streams to find trends, discover anomalies, and provide insights. The insights are then presented to decision-makers through intuitive dashboards, visualizations, or alerts, allowing them to make timely and educated decisions. Despite its promise, IoT-integrated DSS faces numerous hurdles. One key concern is data security and privacy. The expansion of networked devices broadens the attack surface, making IoT ecosystems vulnerable to cyber threats and data breaches. Sensitive data must be safeguarded using robust encryption, authentication, access controls, and privacy-preserving techniques. A further challenge is data adaptability and integration. IoT systems consist of a vast set of hardware framework from several manufacturers, each with its own proprietary protocols and data formats. To achieve smooth interoperability and integration, standard protocols, APIs, and middleware solutions must be developed. Furthermore, scalability, dependability, high availability, and fault tolerance are critical for preventing interruptions in decision support systems. The human aspect is equally crucial. Regardless of how complicated IoT-integrated DSS are, human decision-makers remain critical to the process. Designing user-friendly interfaces, offering training, and decision-making are all essential for the successful acceptance and use of these technologies. Looking ahead, the future of IoT-integrated DSS looks promising. Advances in edge computing, artificial intelligence, and blockchain technology will have the potential to improve these systems’ capabilities and also its durability. Edge computing offers data processing and analysis on a real-time basis at the network’s edge, which reduces the basic latency and bandwidth requirements for data processing. Artificial intelligence, particularly deep learning algorithms, enables more accurate predictions and insights from IoT data. Blockchain technology improves data integrity, transparency, and traceability, hence improving trust in decision support outcomes. IoT-integrated Decision Support Systems (DSS) represent a significant advancement in decision-making processes by combining IoT data with sophisticated analytics, enabling quicker, more informed, and actionable decisions. Addressing challenges related to security, interoperability, scalability, and human factors is crucial for fully leveraging these systems and ushering in a new era for decision-making across various industries. The paper explores the significance of IoT-integrated DSS, focusing on their design, challenges, and potential future applications.