This systematic review summarizes the current state of research on blood cell image segmentation and classification using deep learning techniques. The study focuses on the segmentation, classification, feature extraction, and evaluation of white blood cells (WBCs) and red blood cells (RBCs) in medical images. The review highlights the challenges and trends in the field, including the use of manual image acquisition, the preference for morphological features, and the lack of standardized datasets. The study also discusses the performance of various segmentation and classification techniques, emphasizing the importance of accurate feature extraction and evaluation metrics. The results show that WBC segmentation has a higher accuracy rate compared to RBC segmentation, with many studies relying on manual image collection rather than predefined datasets. The review also identifies the need for standardized, high-quality datasets to improve the diagnostic capabilities of these techniques. The study concludes that the effective use of computer-aided diagnostic (CAD) techniques can significantly enhance the accuracy of blood-related disease diagnosis. The review provides a comprehensive overview of the techniques used in blood cell segmentation and classification, including the use of deep learning algorithms, image processing techniques, and evaluation parameters. The study emphasizes the importance of feature extraction and the role of morphological features in classification. The review also highlights the need for further research in this area, particularly in the development of more accurate and efficient segmentation and classification techniques. The study is intended for a diverse audience, including machine learning researchers, AI experts, medical image processing specialists, and healthcare professionals. The review aims to provide a comprehensive understanding of the techniques used in blood cell segmentation and classification, ultimately assisting clinicians and patients in the diagnosis of blood-related disorders.This systematic review summarizes the current state of research on blood cell image segmentation and classification using deep learning techniques. The study focuses on the segmentation, classification, feature extraction, and evaluation of white blood cells (WBCs) and red blood cells (RBCs) in medical images. The review highlights the challenges and trends in the field, including the use of manual image acquisition, the preference for morphological features, and the lack of standardized datasets. The study also discusses the performance of various segmentation and classification techniques, emphasizing the importance of accurate feature extraction and evaluation metrics. The results show that WBC segmentation has a higher accuracy rate compared to RBC segmentation, with many studies relying on manual image collection rather than predefined datasets. The review also identifies the need for standardized, high-quality datasets to improve the diagnostic capabilities of these techniques. The study concludes that the effective use of computer-aided diagnostic (CAD) techniques can significantly enhance the accuracy of blood-related disease diagnosis. The review provides a comprehensive overview of the techniques used in blood cell segmentation and classification, including the use of deep learning algorithms, image processing techniques, and evaluation parameters. The study emphasizes the importance of feature extraction and the role of morphological features in classification. The review also highlights the need for further research in this area, particularly in the development of more accurate and efficient segmentation and classification techniques. The study is intended for a diverse audience, including machine learning researchers, AI experts, medical image processing specialists, and healthcare professionals. The review aims to provide a comprehensive understanding of the techniques used in blood cell segmentation and classification, ultimately assisting clinicians and patients in the diagnosis of blood-related disorders.