In the rapidly evolving Internet of Things (IoT) domain, artificial intelligence (AI) has transformed IoT systems into self-sufficient entities capable of making groundbreaking autonomous decisions. This paradigm shift has been bolstered by a remarkable increase in computational efficiency, allowing for AI integration, even within stringent resource limitations. However, implementing AI at the edges of such networks remains a significant financial challenge. IoT ecosystems comprising myriad devices, sensors, and actuators are inherently diverse in temporal and data profiles and communication protocols. This diversity predisposes these systems to a spectrum of anomalies. We introduce an innovative anomalydetection framework for sensor anomalies in IoT environments to address this critical challenge. Our methodology was rigorously evaluated using real-time temperature and humidity sensor data and a standard dataset provided by Schneider Electric. We leveraged four cutting-edge machine learning models— Logistic Regression, Random Forest, XGBoost, and AdaBoost—to gauge the framework’s efficacy. Furthermore, our system was tested against the established FogDLearner framework using a PureEdgeSim Simulator to replicate a fog-computing scenario. These results were compelling. The accuracy rates for the real-time sensor data were 98.00%, 98.65%, 99.00%, and 99.21% for Logistic Regression, Random Forest, XGBoost, and AdaBoost, respectively. For the Schneider Electric standard dataset, the models achieved accuracies of 99.00, 99.99, 99.98, and 99.99%, respectively. These findings underscore the robustness of our framework and highlight the potential of fog-enabled machine learning in revolutionizing anomaly detection in IoT environments.