To address challenges in data collection and classification within IoT-enabled wireless sensor networks (WSNs), this study introduces an advanced method that improves data gathering, processing, and management. Despite the transformative integration of IoT into WSNs, communication gaps and inefficiencies due to diverse data sources remain significant. The proposed approach employs cutting-edge data processing techniques to enhance precision, efficiency, and cost-effectiveness while facilitating seamless communication among network components. Key innovations include real-time optimization of data collection and classification processes, adaptive performance in dynamic environments, and robust error detection and correction mechanisms to ensure reliability. This method significantly boosts system performance and resilience, making it a vital advancement in IoT-driven WSNs.