<p>A Wireless Sensor Network (WSN) incorporated with the Internet of Things (IoT) enables collaborative data collection, exchange, and processing. This integration, combined with cloud platforms, supports large-scale real-time applications. Moreover, efficient scheduling and Load Balancing (LB) are essential to prevent congestion and overhead. However, existing research works did not concentrate on Power On Self Test (POST) errors during LB, leading to inefficient resource use and poor network performance. Therefore, this paper presents the enhanced framework of dynamic Task Scheduling (TS) and LB utilizing Sparsemax Activated-Restricted Boltzmann Machine (SA-RBM) in cloud WSN-IoT. Initially, IoT nodes are clustered via K-Means, and reliable Cluster Heads (CHs) are selected by the Trust Residual-Hyper Bound Policy (TR-HBP) to diminish energy consumption and overhead. Here, the CH monitoring phase balances the workload. After that, users submit tasks through applications. The tasks are first balanced by a load balancer, followed by feature extraction. Then, based on the extracted features, suitable and non-suitable tasks are classified using SA-RBM. Afterward, suitable tasks are analyzed and further given to a local container queue in the Scalability Task Queuer (STQ). From this local container, the tasks are arranged to an optimal Virtual Machine (VM) based on makespan time using Secant Wild Horse Optimizer (SWHO). This execution is monitored until the delay expires. If any hibernated state is identified while monitoring, then the execution is migrated to dynamic scheduling by utilizing the fuzzy algorithm. The proposed SA-RBM attains 98% accuracy, which is superior to the existing models.</p>