Automatic tuning of gate-defined semiconductor Quantum Dots (QDs) is a key bottleneck on the path toward scalable qubit architectures. In this thesis, we develop and validate a Machine Learning (ML)-driven pipeline for offline and prospective online charge state auto-tuning, using Charge Stability Diagrams (CSDs) to locate the single charge regime. We assemble and manually annotate a large dataset of CSD images from nine distinct device designs fabricated across multiple process batches and patterned on different wafers and die locations. A U-Net–based Convolutional Neural Network (CNN) is trained to segment charge transition lines under challenging, low-contrast cryogenic conditions and measurement noise. Through five-fold cross-validation, our model achieves a success rate of 80.0% in locating the single charge regime tested on a total of 1015 CSDs.