This work presents the design and implementation of an automated medical image post-processing system, integrated into a PACS using technologies compliant with the international DICOM standard. The system enables automatic querying, retrieval, processing, and storage of medical studies through Python-based algorithms, ensuring flexibility and reproducibility across various clinical settings. Key functionalities include mechanisms for structured clinical data integration and workflow task automation, which contribute to reducing manual intervention and enhancing consistency. The solution was deployed in a real clinical environment using open-source software and standardized tools, demonstrating effective integration with existing imaging modalities and workstations. In addition to automating repetitive tasks, the system supports the storage of not only medical images but also associated documentation, in accordance with DICOMWeb specifications. The results demonstrate significant improvements in operational efficiency, a reduction in human error, and scalability to large volumes of imaging data. This development enhances workflow optimization in medical imaging environments and provides a robust foundation for future implementations, including the integration of artificial intelligence techniques in clinical decision support systems.