This systematic review provides a comprehensive overview of blood cell image analysis techniques, focusing on segmentation, classification, feature extraction, and dataset utilization. The study aims to enhance the diagnostic accuracy of hematological disorders such as leukemia, anemia, lymphoma, and thalassemia by leveraging computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. The review covers four main areas: segmentation techniques, classification methodologies, descriptive feature selection, and evaluation parameters. Key findings include the preference for manual image collection over predefined datasets, the popularity of morphological features in feature selection, and the need for standardized, high-quality datasets to improve diagnostic capabilities. The study also highlights the importance of morphological features in future research and the potential of deep learning techniques in blood cell analysis. The review is intended for researchers, clinicians, and professionals in machine learning, medical imaging, hematology, and bioinformatics, providing a detailed understanding of the current state-of-the-art techniques and their applications in blood cell analysis.This systematic review provides a comprehensive overview of blood cell image analysis techniques, focusing on segmentation, classification, feature extraction, and dataset utilization. The study aims to enhance the diagnostic accuracy of hematological disorders such as leukemia, anemia, lymphoma, and thalassemia by leveraging computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. The review covers four main areas: segmentation techniques, classification methodologies, descriptive feature selection, and evaluation parameters. Key findings include the preference for manual image collection over predefined datasets, the popularity of morphological features in feature selection, and the need for standardized, high-quality datasets to improve diagnostic capabilities. The study also highlights the importance of morphological features in future research and the potential of deep learning techniques in blood cell analysis. The review is intended for researchers, clinicians, and professionals in machine learning, medical imaging, hematology, and bioinformatics, providing a detailed understanding of the current state-of-the-art techniques and their applications in blood cell analysis.