The paper introduces iDetect, a project that develops an intricate Anomaly Detection System (ADS) for temperature sensor data monitoring in Industrial Internet of Things (IIoT) networks. The ADS identifies anomalies, particularly temperature spikes or drops, to safeguard industrial operations’ integrity and efficiency. After rigorous testing, the Isolation Forest Classifier, with a precision of 0.46, recall of 1.0, and an F1-score of 0.63, emerged as the most accurate algorithm with a 95% accuracy rate. Comparatively, One-Class SVM and LSTM achieved 87% and 92% accuracy, respectively. The paper outlines the developmental trajectory, including testing in static environments and a forward-looking perspective on transitioning to real-time monitoring for enhanced efficiency. It concludes with a discerning evaluation, emphasizing iDetect’s indispensable role in fortifying anomaly detection within IIoT networks, particularly for temperature sensor data in complex industrial settings.