In recent days, the concept of the Internet of Things (IoT) has emerged as a prominent research area, significantly contributing to the development of advanced intelligent applications. The execution of challenging and time-sensitive tasks has become more difficult due to the increased adoption of IoT and the large volumes of data it generates, leading to high power consumption time. Hence, this research proposes the Radial Basis Function-based Support Vector Machine (SVM) based on the IoT-enabled big data framework for securing the data in edge-cloud computing (ECC). Data are collected through different IoT sensors like conservational perceiving, privacy managing, capability managing, power perceiving, conveyance, as well as traffic. The edge intelligence model was introduced for the procedure of storing big data at network edges with a combination of cloud expertise. The Optimized Yet Another Resource Negotiator (OYARN) was utilized for cluster management. A data inoculation, as well as storage, was taken by MapReduce parallel approach. From the result analysis, the proposed RBF-SVM attains an accuracy of 91.38% when compared to the Backpropagation Neural Network (BPNN).