Battery testing is fast paced and highly error prone, both from human interactions and test equipment faults. Our solution uses machine learning based early failure detection to identify abnormal behavior before it becomes costly. A dedicated battery emulator captures and reproduces real test profiles, enabling accurate training and validation of the detection system. Tested across major chemistries (NCA, LFP), the method detects temperature related anomalies with 97% accuracy with the offline data. Additional AI driven systems are being developed to detect hardware malfunctions and incorrect global limits or chemistry settings, helping prevent testing errors as early as possible.