This form of automation endows devices with intelligence, enabling them to assimilate data through sensor nodes and formulate informed decisions. Traditionally, data mining has been portrayed as a safeguard against threats or malfunctions in networks. The primary objective here is to minimize redundancy in information packets recognised by nodules, subsequently transmitted to the cluster head for pre-processing. In the realm of IoT, the sensor node network operates in two dimensions: spatial detection and collection of data by nearby nodes, followed by temporal monitoring at specified intervals. Regardless of the scenario, the proposed approach stands out as the optimal choice. It emphasizes the elimination of redundant characteristics in the detected data, employing a packet classification method for filtering and essential packet mining. The suggested novel data mining (NDM) approach in this study not only supports QoS (Quality-of-Service) for diverse requests and amenities but also facilitates capacity paired, traffic flow accounting, and the observing of recognised information. The effectiveness of an NDM-based Wireless Sensor Network (WSN) tailored for IoT is gauged through various metrics. Simulation results unequivocally underscore the superior performance of the suggested strategy compared to alternative methods. Future endeavours aim to implement this framework in dynamic settings, creating an application tailored to real-time problem-solving. © 2024 Elsevier B.V., All rights reserved.