Sickle cell disease (SCD) is a type of hematological disorder which leads to blood vessel occlusion accompanied by painful life episodes and even death. The diverse red blood cell (RBCs) shapes in SCD patients reveal important biomechanical and biorheological characteristics such as density, fragility, and adhesive properties. Therefore, having an objective and effective method for quantifying and classifying RBC shapes will provide better insights and ultimately improve the prognosis of the disease. The detection of sickle cell disease represents a critical task in medical image analysis, requiring a thorough examination for accurate diagnosis and subsequent classification of irregularities. This review paper focuses on various existing state-of-the-art methods, recent developments, and works in the sickle cell disease detection, segmentation, and classification. This paper mainly focuses on the challenges encountered during the segmentation of overlapping blood cells. Additionally, the review discusses standard validation measures employed for performance analysis of other existing methods. The methodologies and experiments presented in this review are anticipated to be valuable for further research and advancements in this important medical imaging domain.