Radiotherapy remains a cornerstone in cancer management, relying heavily on the precision and reliability of linear accelerators to ensure safe and effective treatments. To maintain high standards, robust quality assurance (QA) protocols are particularly critical, especially for image-guided systems. This work presents the implementation of an automated system for quality control analysis of these imaging technologies. Traditional manual QA processes are commonly labor-intensive and subject to human variability. Guided by the AAPM Task Group 198 recommendations for MV/kV planar and CBCT imaging systems, this project implemented an automated analysis workflow using the QATrack+ platform alongside the Pylinac Python library. This integration improves QA efficiency by automating image evaluation and data recording. The system offers greater consistency and accuracy compared to traditional approaches, while the QATrack+ platform enables performance monitoring over time, facilitating the detection of trends or anomalies that could compromise imaging fidelity and treatment accuracy.