The rapid advancement of scientific imaging technologies has created significant challenges in managing large-scale image datasets while maintaining compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. We present Marimba, an open-source Python framework for structuring, processing, and packaging scientific image datasets. Marimba enhances data management through unified workflow processing, automated metadata embedding, efficient data handling, and standardized dataset packaging while integrating with the image FAIR Digital Object (iFDO) metadata standard. The framework's capabilities were evaluated through four diverse marine case studies involving multi-instrument microscopy, automated plankton imagery, deep-sea coral surveys, and historical image digitization. Marimba successfully processed datasets ranging from thousands to hundreds of thousands of images and videos, demonstrating robust performance and scalability. Marimba's modular architecture enables customization for specific research requirements while ensuring consistent data management practices. Results demonstrate Marimba's potential to advance scientific image data management by improving workflow efficiency, data quality, and adherence to FAIR principles throughout the research data lifecycle.