Integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RISs) are viewed as promising technologies for the future 6G wireless networks. ISAC designs assisted by RISs are particularly attractive for tracking and localization problems in internet of everything (IoE) applications. In particular, RISs can be deployed to realize a smart radio environment (SRE) that tracks the user equipment (UE) in blind spaces, i.e., where the direct line-of-sight (LoS) wireless link is not available. This paper proposes a deep learning framework to integrate RIS-assisted mobile UE localization and communication in the 60 GHz band. The number of RIS and their electronic steering angles are investigated to support both localization and communication processes implemented on shared time resources. The UE localization is obtained through DL algorithms based on convolutional neural networks (CNN) and vision transformers (ViT) structures. The proposed algorithms are trained using a wide variety of physical parameters such as number of RIS steering angles, RIS area size, and number of antenna at the base station (BS). The system performance is measured in terms of achieved positioning root mean squared error (RMSE), algorithm complexity, and inference time. A Cramér-Rao bound for estimating the localization error based on RISs deployment, is also provided. Localization accuracy, frame efficiency and throughput tradeoffs are explored for different IoT setups.