Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3945 2019 2019 Assistant Professor, Department of AIML & IoT, VNR Vignana Jyothi Institution of Engineering and Technology, Hyderabad, Telangana, India Jyotsnarani Jyotsnarani Assistant Professor, Department of Computer Science and Engineering, Mallareddy University, Hyderabad, India T. Krishna Murthy Assistant Professor (Sr Gr), Department of Computer Science and Business Systems, Nehru Institute of Engineering and Technology, Coimbatore, India S. Manjula Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad-500043, Telangana, India Sukanya Ledalla Assistant Professor, Department of Computer Science and Engineering, Aditya University, Surampalem, India Alla Rajendra Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India P. Lakshmi Harika Department of Electrical and Electronics Engineering, Aarupadai veedu Institute of Technology,Vinayaka Missions Research Foundation(DU), Chennai Campus, Paiyanoor:603104, India K Boopathy The consistent improvement of remote sensing (RS) technology has resulted in an easy access to a large volume of satellite imagery. There is a need for effective and scalable solutions for widening the application of RS in different fields and making it work efficiently in practical situations. This research propose novel technique in satellite image gathering and cloud IoT network risk management using machine-learning model. Here the cloud IoT network has been used in satellite image collection and this network security analysis has been carried out using secure trust based cryptographic blockchain model. Then this collected image has been classified using convolutional bayes fuzzy markov perceptron basis function model. Experimental analysis has been carried out in terms of accuracy, QoS, recall, latency, scalability. Proposed model attained accuracy of 97%, QoS of 94%, LATENCY of 96%, Scalability of 95%, RECALL of 93%. These results assist decision-makers, planners, and scientists studying remote sensing select an appropriate image classification system for tracking a dynamic, fragmented, and varied landscape. 2026 2026 207 217 10.54216/JISIoT.180115 https://www.americaspg.com/articleinfo/18/show/3945