Accurate identification and classification of intestinal parasitic eggs are essential for effectively diagnosing and treating parasitic infections. Traditional manual microscopic diagnosis methods are time-consuming and prone to errors. Recent advancements in technology have shown potential in automating this process, yet more advanced and accurate methods are needed to overcome existing challenges. The proposed research aims to develop a robust and efficient approach for intestinal parasitic egg segmentation and classification using the Mask R-CNN algorithm. The research begins with an extensive review of existing literature on intestinal parasitic infections, their impact, and the limitations of current diagnostic methods. It further explores the principles of image processing, medical imaging techniques, and the fundamentals of the Mask R-CNN algorithm. The proposed work involves accessing a dataset comprising 10 thousand images of 10 different types of parasitic eggs from IEEE and preprocessing them to enhance their quality. The Mask R-CNN algorithm is then trained on this dataset, enabling it to accurately segment and classify intestinal parasitic eggs. Performance evaluation uses quantitative measures such as Precision, recall, and F1-score (shown in Table [1](#tbl-cap-0001)). The results demonstrate the effectiveness of the Mask R-CNN algorithm in segmenting and classifying intestinal parasitic eggs, achieving an overall accuracy of 95%. These findings contribute to intestinal parasitic egg analysis by providing an advanced and automated approach for SegmentationSegmentation and classification. Future research endeavors could expand the dataset, optimise computational efficiency, and integrate the developed algorithm into practical diagnostic tools.