In the Internet of Things, information fusion is among the crucial problems and probably occurs due to the dense deployment of consumer electronic devices. In the literature, various methodologies have been developed to fine-tune raw data; however, consumer electronic devices’ neighborhood information has been completely ignored. In this manuscript, a machine learning and neighborhood-assisted fusion approach has been developed for consumer electronic devices to ensure that captured data values have been properly refined before onward processing at the respective edge. In this approach, every server accepts member request invitations from electronic devices deployed in its coverage area. It applies the well-known K-mean and supports vector machine (SVM) algorithms to refine captured data values by consumer electronic devices. Apart from that, the server module has the built-in intelligence to compare the captured data values of those electronic devices, which reside nearby and probably have a higher redundancy ratio. Simulation results have concluded that the proposed machine learning-assisted fusion approach is an ideal solution for the IoT in general and the Artificial Intelligent-enabled IoT in particular. Additionally, the proposed algorithm was thoroughly examined via various performance evaluation metrics such as lifetime, energy efficiency, and refinement ratio, where it has shown convincing results such as 30% improvement in the fusion ratio.