Resistance Spot Welding (RSW) is one of the joining techniques widely used in different industries, mainly in the automotive industry. It is popular because of its ease of use, speed, reliability, cost-effectiveness, and opportunity for automation. Traditionally, destructive testing methods are used to assess the quality of the welds, which are time-consuming, often impractical for real-time monitoring, and expensive. To overcome these challenges, this thesis proposed a novel way to predict nugget size based on Machine Learning (ML) methodology, using features derived from electrode force signals processed through the Fast Fourier Transform (FFT). The research begins by analyzing 50 records of electrode force signals in frequency domains.