Ultrasonic metalMetals welding (USMW) is a technologyDissimilar metals for producing solid joints of electrical connectors, such as wires and terminals. With the ongoing substitution of copperCopper with aluminumAluminum in conductors for weight savings, USMW sees application in automotiveAutomotive, aerospaceAerospace and other industriesIndustry. Despite its widespread use, USMW lacks sufficient process monitoringProcess monitoring. Currently, industrial process monitoringProcess monitoring relies on sample-based destructive testing. Monitoring all joints and avoiding false positives is unachievable with this method, leading to significant pseudo-rejects and undetected faulty welds. This work focuses on developing a process monitoringProcess monitoring system using machine learningMachine learning (ML) for analyzing USMW machine data. Utilizing ML, the system aims to classify weld quality based on this data, reducing scrap, and pseudo-scrap rates. The work involves generating training data for weld faults, configuring an ultrasonic weldingUltrasonic welding machine for data collection, and evaluating ML methods for accurate classification. Preliminary results indicate 99.8% accuracy, enhancing reliability and efficiency of USMW.