The agriculture that is enabled with the Internet of Things (IoT) in addition to Wireless Sensors Networks (WSN) can aid the farmers in monitoring their product along with conditions in real-time. The indispensable issues that one has to confront with WSN are Energy-saving along with effective bandwidth usage. Lately, for the amelioration of network stability together with lifetime, disparate cluster-centered solutions are modeled. Nevertheless, most techniques are merely modeled in an energy-aware manner and utilize merely a distance parameter aimed at the data communication. It is imperative for satisfying the bandwidth use of IoT. Here, a cluster-centered energy as well as bandwidth aware routing model is proposed for agriculture data on IoT-centered WSN. Optimal Clusters Head (CH) selection as well as clustering is performed on the WSN for carrying out cluster-centered Data Aggregation (DA). The novel chaos mapping and Opposition centered Learning Grasshopper Optimization Algorithm (CO2GA) chooses the collection of CH as of agricultural Sensor Nodes (SN). Next, centered upon the distance betwixt the chosen CH and SN, the clusters are generated. The clusters share the data to their CH, and the Chaos key generated Advanced Encryption Standard with Rivest–Shamir–Adleman (CKAES-RSA) algorithm encrypt the aggregated data of CH. Lastly, the encrypted data of IoT data are shared with the Base Stations (BS) or Sink Node (Sn) by means of the optimal routing. Aimed at Optimal Route Selection (ORS), the paper utilizes a Deep Learning (DL) approach, explicitly crossover and mutation-based optimal Multi-Layer Perceptrons (CM-OMLP), which computes the fitness of hidden layer by regarding energy, bandwidth, trust, delay, along with congestion level. The proposed encryption and routing mechanism’s results are weighted against the first-rate techniques. The proposed work achieves the highest level of security aimed at the IoT data and fulfills the QoS requirements regarding packet delivery, throughput, delay, along with Network LifeTimes (NLT).