For many years, traditional rigid PCBs have been utilized in many applications and have been shown to be reliable in a number of applications. There hasn’t been much research on flexible electronics device attachment techniques and process reliability. There has been prior research done on copper sintering using various methodologies, but there is no prediction model to apply the underlying information directly for predicting the performance of the printed electronics. The sintering process determines the electrical performance of printed traces, and it is necessary to comprehend and estimate the resistivity of printed traces. This study developed a regression model based on an Artificial Neural Network (ANN) to predict resistivity. Because flexible substrates allow for more flexibility, it is critical to create a reliable way of attaching components to circuits that can endure various motions. Micro dispensing equipment was employed in this investigation to print conductive traces, an electrically conductive adhesive (ECA), and low-temperature solder (LTS) for component attachment pads. There is little understanding of SMD attachments’ behavior on additively printed flexible substrates, and we examined different aspects of their performance in this study.