Fault detection in wireless body sensor data is crucial for accurate clinical decisions, particularly in cardiac care. Issues such as sensor misplacement, connection errors, and patient movement can degrade data quality, necessitating timely detection to ensure reliability. To address this, an AI-based approach for detecting faults in ECG sensor data. We used the AD8232 ECG sensor with an ESP8266 microcontroller to collect body ECG data. A sliding window protocol is used for preprocessing to segment the ECG signal into fixed-duration windows, allowing fault detection and classification. The findings show that the proposed approach effectively identifies difficult-to-distinguish faults, ensuring reliable and accurate fault detection through advanced machine learning and robust preprocessing. Algorithms such as Random Forest, Support Vector Machine, and Extra Tree are used to identify faults and evaluate their impact. Among these, Extra Trees achieves the highest accuracy of 98.06%, demonstrating its effectiveness in reliable fault detection, improving data quality, and clinical outcomes.