In this era of rapid technological progress, wireless technology has demonstrated immense potential, particularly in data transmission. One promising domain for harnessing the power of wireless technologies is traffic control. IoT (Internet of Things) has garnered significant attention, as it encourages devices to collaborate and share services and data. Wireless Sensor Networks (WSNs) play a crucial role within the IoT framework by facilitating data transmission. Routing, a fundamental strategy, involves establishing routes and transmitting data packets to destination from source within the networks. As IoT networks continue to expand, the challenge of maintaining security grows increasingly complex. Secure routing mechanisms must adapt to accommodate the growing number of devices and potential security threats. In this research, we introduce the concept of cluster-based trusted secure aware routing for WSN-Assisted IoT using a deep learning technique (CTSR-DL). Our approach involves the development of the enhanced chaos game optimization (ECGO) algorithm, which efficiently balances the load by clustering nodes within the network. The trust level of nodes is calculated using various metrics, including mobility, received signal strength (RSS), and congestion rate. Furthermore, we employ the convolutional neural network-bagged decision tree (CNN-BDT) for optimal path selection between sources and destinations. For assessing our proposed CTSR-DL approach performance, we conducted various simulation scenarios. The results clearly demonstrate the effectiveness of our approach when compared with existing routing methods.