Detecting abnormal behaviors in Internet of Things environments allows maintaining working states of the networked systems and can prevent significant disturbances, and thus is a crucial activity. This study focuses by proposing a deep learning approach by RNNs for early detection of anomalies in the IoT systems. The suggested model applies state-of-the-art deep learning techniques on text-based data feeds generated through the various IoT devices to detect any anomalous behavior which may be indicative of possible security incidents, problems, or other failure modes in the systems. What we are showing in this paper is that our model can accurately and reliably identify anomalous activity within the context of the Internet of Things. From confusion matrices, classification reports among other visualization tools gives an insight of the model decision-making process and the reasons as to why certain abnormalities are detected. As seen in this study, deep learning can enhance the robustness and sustainability of IoT systems with implications to the improvement of efficiencies in system management and security procedures in advancing IoT environments.